eBay:iPads:: sold classification:: spdiff
bdanalytics
Date: (Tue) Jul 28, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "spdiff_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold"
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_var <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCL0UD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"2nd"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","antenna","anti","anyone", "area","arizona","att"
,"beginning","bidder","bonus","boot","bound","bruises"
,"changed","changing","chrome"
,"confidence","considerable","consumer","contents","control","cream"
,"date","daughter","decent","defender","defense","degree","depicted"
,"disclaimer","distressed","divider"
,"dlxnqat9g5wt","done","dont","durable","dust","duty"
,"either","erased","ereader","essentially","every","exact"
,"faint","film","final","flickers","folding"
,"generic","genuine","glitter","goes"
,"half","handstand","hdmi","high","higher","hole","hospital"
,"impact","instead","interior"
,"jack","july"
,"keeps","kind","known"
,"last","late","let","letters","level","lifting","limited","line","lining","liquid"
,"local","long","longer","looping","loss"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll","mgye2ll"
,"middle", "mind","mixed"
,"neither","none","november"
,"occasional","online","outside"
,"paperwork","period","pet","played","plug","poor","portion","pouch","price","provided"
,"ranging"
,"recently","red","reflected","repeat","required","reserve","residue","result"
,"roughly","running"
,"said","seconds","seem","semi","send","serious","setup"
,"shell","short","size","slice","smoke","smooth"
,"softer","software","somewhat","soon"
,"sparingly","sparkiling","special","speed"
,"stains","standup","status","stopped","strictly","subtle","sustained","swappacom"
,"technical","tempered","texture","thank","therefore","think","though"
,"toddler","totally","touchy","tried","typical"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","winning","wrapped"
,"zaag","zero", "zombie"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_filter_txt_terms <- "top" # or "sparse"
glb_top_n <- c(10)
names(glb_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "description", "startprice"
#, "startprice.log", "sold"
)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE
glb_cluster.seed <- 189 # or any integer
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.081 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here")
glb_drop_obs <- c(
11234, #sold=0; 2 other dups(10306, 11503) are sold=1
11844, #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
NULL)
glb_allobs_df <- glb_allobs_df[!glb_allobs_df[, glb_id_var] %in% glb_drop_obs, ]
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
spd_allobs_df <- read.csv(paste0(glb_out_pfx, "sp_predict.csv"))
if (nrow(spd_allobs_df) != nrow(glb_allobs_df))
stop("mismatches between spd_allobs_df & glb_allobs_df")
mrg_allobs_df <- merge(glb_allobs_df, spd_allobs_df)
if (nrow(mrg_allobs_df) != nrow(glb_allobs_df))
stop("mismatches between mrg_allobs_df & glb_allobs_df")
mrg_allobs_df$startprice.diff <- mrg_allobs_df$startprice -
mrg_allobs_df$startprice.predict.
print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff",
colorcol_name = "biddable"))## Warning in myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff", :
## converting biddable to class:factor
print(myplot_histogram(mrg_allobs_df, "startprice.diff",
fill_col_name = "biddable"))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
glb_allobs_df <- mrg_allobs_df
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
"startprice.log", "startprice.predict.")
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# Only for _sp
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
# glb_allobs_df$.src <- "Test"
# glb_allobs_df[!is.na(glb_allobs_df$sold) & (glb_allobs_df$sold == 1), ".src"] <- "Train"
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
###
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 10.081 15.291 5.21
## 2 inspect.data 2 0 15.292 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 999 860 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5373857 0.4626143 NA
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## sold sold.fctr .n
## 1 0 N 999
## 2 1 Y 860
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 999 860 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5373857 0.4626143 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: startprice.diff"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 15.292 20.094 4.802
## 3 scrub.data 2 1 20.094 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
sel_obs <- function(vars_lst, ignore.case=TRUE, perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
# if (!is.null(Popular)) {
# if (is.na(Popular))
# tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
# tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
# }
# if (!is.null(NewsDesk))
# tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
for (var in names(vars_lst)) {
if (grepl(".contains", var))
tmp_df <- tmp_df[grep(vars_lst[var],
tmp_df[, unlist(strsplit(var, ".contains"))],
ignore.case=ignore.case, perl=perl), ]
else
tmp_df <- tmp_df[tmp_df[, var] == vars_lst[var], ]
}
return(glb_allobs_df[, glb_id_var] %in% tmp_df[, glb_id_var])
}
#print(glb_allobs_df[sel_obs(list(description.contains="mini(?!m)"), perl=TRUE), "description"])
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cols),
FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE)
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1593 0 0 0 0 0
## 1 288 0 4 36 28 172 196
## Unknown 4 4 2 0 0 330 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1597 0 0 0 0 0
## 1 292 0 6 36 28 172 196
## Unknown 0 0 0 0 0 330 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 20.094 20.863 0.769
## 4 transform.data 2 2 20.863 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10178, 10948, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
glb_allobs_df[row_pos, "descr.my"] <-
gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 20.863 21.414 0.551
## 5 extract.features 3 0 21.414 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 21.422 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 21.422 21.44
## 2 extract.features_factorize.str.vars 2 0 21.440 NA
## elapsed
## 1 0.018
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid prdline.my
## "storage" "productline" ".src" ".grpid" "prdline.my"
## descr.my
## "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_stop_mtrx > 0)
tmp_allobs_df$terms.n.post.stop.log <- log(1 + tmp_allobs_df$terms.n.post.stop)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_stop_mtrx)
print(sprintf(" Top_n stem TfIDf terms for %s:", txt_var))
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stem_mtrx
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$terms.n.post.stem.log <- log(1 + tmp_allobs_df$terms.n.post.stem)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(txt_var_pfx, c("terms.n.post.stop", "terms.n.post.stem")))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_filter_txt_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_filter_txt_terms == "top") {
txt_X_df <- txt_full_X_df[, terms_full_df$pos[1:glb_top_n[[txt_var]]], FALSE]
} else stop("glb_filter_txt_terms should be one of c('sparse', 'top') vs. '",
glb_filter_txt_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 21.440 21.701
## 3 extract.features_process.text 3 0 21.701 NA
## elapsed
## 2 0.261
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 178): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 178): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 178): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 178): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 178): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 178): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 178): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 178): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 178): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 178): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 178): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 178): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 178): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 178): #\\bWall Street#..."
## [1] "running gsub for 150 (of 178): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 178): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 178): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 6
## [[1]]
## [1] 3
## attr(,"match.length")
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## attr(,"useBytes")
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## attr(,"capture.start")
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## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
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## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
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## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
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## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
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## [1] "ROKEN SCREEN"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
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## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
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## pattern .n
## 1 CONDITION. 8
## 2 ONLY. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CORNER. 2
## 6 ESN. 2
## 7 GOOD. 2
## 8 ICLOUD. 2
## 9 IPADS. 2
## 10 LOCKED. 2
## 11 LOCKS. 2
## 12 ONLY. 2
## 13 SCRATCHES. 2
## 14 TEARS. 2
## 15 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 21.701 23.366 1.665
## 4 1 23.366 NA NA
## [1] "Remaining compound terms in descr.my: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 23.366 23.37 0.004
## 5 0 23.371 NA NA
## [1] "Building glb_corpus_lst..."
## [1] " Top_n stop TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 729; Cols: 4"
## TfIdf term freq pos
## condition 208.1205 condition 498 149
## new 125.5866 new 156 433
## used 123.4473 used 240 695
## good 120.9670 good 197 289
## scratches 114.0567 scratches 254 570
## screen 106.6170 screen 210 572
## TfIdf term freq pos
## scratch 30.068378 scratch 25 568
## days 6.562908 days 4 184
## taken 5.878172 taken 6 649
## outer 5.557938 outer 5 458
## lot 3.602633 lot 2 386
## greeting 2.075117 greeting 2 294
## TfIdf term freq pos
## 975 1.1375583 975 1 16
## blemish 1.1375583 blemish 1 83
## cables 1.1375583 cables 1 106
## engravement 1.1375583 engravement 1 226
## handling 1.1375583 handling 1 304
## 79in 0.9479652 79in 1 15
## TfIdf term freq pos
## 975 1.1375583 975 1 16
## blemish 1.1375583 blemish 1 83
## cables 1.1375583 cables 1 106
## engravement 1.1375583 engravement 1 226
## handling 1.1375583 handling 1 304
## 79in 0.9479652 79in 1 15## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 588; Cols: 4"
## TfIdf term freq pos
## condit 208.1066 condit 496 122
## use 146.5910 use 291 559
## scratch 128.3886 scratch 286 457
## new 125.5866 new 156 346
## good 121.0564 good 197 233
## ipad 107.4871 ipad 232 275
## TfIdf term freq pos
## set 17.367419 set 14 469
## purpos 3.372064 purpos 2 418
## first 2.939748 first 2 206
## spent 2.635069 spent 2 503
## oem 2.275117 oem 1 355
## refund 1.421948 refund 1 434
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.n.post.stop -0.084045051
## terms.n.post.stop.log -0.065622097
## TfIdf.sum.post.stop -0.033351783
## terms.n.post.stem -0.083896783
## terms.n.post.stem.log -0.065626873
## TfIdf.sum.post.stem -0.036095697
## terms.n.stem.stop.Ratio 0.016386012
## TfIdf.sum.stem.stop.Ratio -0.008752785
## label step_major step_minor bgn end elapsed
## 5 extract.features_build.corpus 4 0 23.371 34.25 10.879
## 6 extract.features_extract.DTM 5 0 34.250 NA NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 34.250 35.596 1.347
## 7 extract.features_report.DTM 6 0 35.597 NA NA
## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 588)>>
## Non-/sparse entries: 8269/1554047
## Sparsity : 99%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 8)>>
## Non-/sparse entries: 2069/19187
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 35.597 37.677 2.08
## 8 extract.features_bind.DTM 7 0 37.677 NA NA
## [1] "Binding DTM for descr.my..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 37.677 38.115 0.439
## 9 extract.features_bind.DXM 8 0 38.116 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# dsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# dsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# dsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini", glb_rsp_var)))## prdline.my productline D.P.mini sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad 2 iPad 2 0 N 139
## 4 iPad mini iPad mini 0 N 138
## 5 iPad mini iPad mini 0 Y 126
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 121
## 8 iPad mini iPad mini 0 <NA> 108
## 9 iPad Air iPad Air 0 N 102
## 10 iPad 1 iPad 1 0 N 100
## 11 iPad Air 2 iPad Air 2 0 N 100
## 12 iPad 4 iPad 4 0 N 93
## 13 Unknown Unknown 0 <NA> 89
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 81
## 16 iPad 3 iPad 3 0 Y 80
## 17 iPad Air iPad Air 0 Y 78
## 18 iPad Air iPad Air 0 <NA> 74
## 19 iPad 3 iPad 3 0 N 73
## 20 iPad Air 2 iPad Air 2 0 Y 71
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad Air 2 iPad Air 2 0 <NA> 62
## 24 iPad mini 3 iPad mini 3 0 N 61
## 25 iPad mini 2 iPad mini 2 0 N 56
## 26 iPad 3 iPad 3 0 <NA> 55
## 27 iPad mini 2 iPad mini 2 0 <NA> 52
## 28 iPad mini 2 iPad mini 2 0 Y 48
## 29 iPad mini 3 iPad mini 3 0 <NA> 35
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad mini iPad mini 1 N 7
## 32 iPad mini iPad mini 1 Y 5
## 33 iPad mini 2 iPad mini 2 1 <NA> 4
## 34 iPad mini Retina iPad mini Retina 0 Y 4
## 35 iPad mini Unknown 1 <NA> 3
## 36 iPad mini iPad mini 1 <NA> 3
## 37 iPad mini 3 iPad mini 3 1 <NA> 3
## 38 iPad mini Retina iPad mini Retina 0 N 3
## 39 iPad mini 2 iPad mini 2 1 N 2
## 40 iPad mini 3 iPad mini 3 1 N 2
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad mini Unknown 1 N 1
## 43 iPad mini Unknown 1 Y 1
## 44 iPad mini iPad mini 2 Y 1
## 45 iPad mini 2 iPad mini 2 1 Y 1
## 46 iPad mini Retina iPad mini Retina 1 N 1glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad Air"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air", glb_rsp_var)))## prdline.my productline D.P.air sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad mini iPad mini 0 N 145
## 4 iPad 2 iPad 2 0 N 139
## 5 iPad mini iPad mini 0 Y 132
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 120
## 8 iPad mini iPad mini 0 <NA> 111
## 9 iPad 1 iPad 1 0 N 100
## 10 iPad Air iPad Air 0 N 98
## 11 iPad Air 2 iPad Air 2 0 N 97
## 12 iPad 4 iPad 4 0 N 92
## 13 Unknown Unknown 0 <NA> 88
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 80
## 16 iPad 3 iPad 3 0 Y 79
## 17 iPad Air iPad Air 0 Y 75
## 18 iPad 3 iPad 3 0 N 73
## 19 iPad Air iPad Air 0 <NA> 73
## 20 iPad Air 2 iPad Air 2 0 Y 69
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad mini 3 iPad mini 3 0 N 63
## 24 iPad Air 2 iPad Air 2 0 <NA> 60
## 25 iPad mini 2 iPad mini 2 0 N 58
## 26 iPad mini 2 iPad mini 2 0 <NA> 56
## 27 iPad 3 iPad 3 0 <NA> 55
## 28 iPad mini 2 iPad mini 2 0 Y 49
## 29 iPad mini 3 iPad mini 3 0 <NA> 38
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad Air iPad Air 1 N 4
## 32 iPad mini Retina iPad mini Retina 0 N 4
## 33 iPad mini Retina iPad mini Retina 0 Y 4
## 34 iPad Air iPad Air 1 Y 3
## 35 iPad mini Unknown 0 <NA> 3
## 36 iPad Air 2 iPad Air 2 1 <NA> 2
## 37 iPad Air 2 iPad Air 2 1 N 2
## 38 iPad Air 2 iPad Air 2 1 Y 2
## 39 iPad 3 iPad 3 1 Y 1
## 40 iPad 4 iPad 4 1 N 1
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad Air Unknown 1 <NA> 1
## 43 iPad Air Unknown 1 N 1
## 44 iPad Air Unknown 1 Y 1
## 45 iPad Air iPad Air 1 <NA> 1
## 46 iPad Air 2 iPad Air 2 2 N 1
## 47 iPad mini Unknown 0 N 1
## 48 iPad mini Unknown 0 Y 1glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# dsp_obs(list(prdline.my="Unknown"), all=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 314
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 314
## 6 iPadmini 2+ 299
## 7 Unknown 285glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 38.116 89.1 50.984
## 10 extract.features_end 9 0 89.100 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 8 extract.features_bind.DTM 7
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 38.116 89.100 50.984 50.984
## 5 0 23.371 34.250 10.879 10.879
## 7 0 35.597 37.677 2.080 2.080
## 3 0 21.701 23.366 1.665 1.665
## 6 0 34.250 35.596 1.347 1.346
## 8 0 37.677 38.115 0.439 0.438
## 2 0 21.440 21.701 0.261 0.261
## 1 0 21.422 21.440 0.018 0.018
## 4 1 23.366 23.370 0.004 0.004
## [1] "Total Elapsed Time: 89.1 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 21.414 90.455 69.042
## 6 cluster.data 4 0 90.456 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 90.456 91.469 1.013
## 7 manage.missing.data 4 1 91.469 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print("Clustering features: ")
print(cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_rsp_var]), method="ML")))
category_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, glb_rsp_var]))
names(category_df)[c(1, 2)] <- c(glb_category_var, glb_rsp_var)
category_df <- do.call(tidyr::spread, list(category_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(category_df),
function(row) entropy(as.numeric(category_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(category_df),
function(row) sum(as.numeric(category_df[row, -1])))
category_df$.entropy <- tmp.entropy; category_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)", glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
print(category_df)
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (grp in sort(unique(glb_allobs_df[, glb_category_var]))) {
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df[, glb_category_var].fctr) / minClusterSize=20)
# which(levels(glb_allobs_df[, glb_category_var].fctr) == grp)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
cluster_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, ".clusterid"],
glb_allobs_df[, glb_rsp_var]))
cluster_df <- subset(cluster_df, Freq > 0)
names(cluster_df)[c(1, 2, 3)] <- c(glb_category_var, ".clusterid", glb_rsp_var)
# spread(unite(cluster_df, prdline.my.clusterid, prdline.my, .clusterid),
# sold.fctr, Freq)
cluster_df <- do.call(tidyr::unite,
list(cluster_df, paste0(glb_category_var, ".clusterid"),
grep(glb_category_var, names(cluster_df)),
grep(".clusterid", names(cluster_df))))
cluster_df <- do.call(tidyr::spread,
list(cluster_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
print(cluster_df)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features[paste0(glb_category_var, ".fctr")] <-
c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr## [1] "Clustering features: "
## [1] "D.T.condit" "D.T.use" "D.T.scratch" "D.T.new" "D.T.good"
## [6] "D.T.ipad" "D.T.screen" "D.T.great" "D.T.work" "D.T.excel"
## [11] "D.P.http" "D.P.mini" "D.P.air"
## [1] "glb_allobs_df Entropy: 0.6903"
## [1] "glb_allobs_df$prdline.my Entropy: 0.6850 (99.2280 pct)"
## prdline.my N Y .entropy .knt
## 1 Unknown 118 80 0.6746159 198
## 2 iPad 1 100 125 0.6869616 225
## 3 iPad 2 141 147 0.6929302 288
## 4 iPad 3+ 166 145 0.6908657 311
## 5 iPadAir 203 150 0.6818332 353
## 6 iPadmini 146 133 0.6920612 279
## 7 iPadmini 2+ 125 80 0.6688571 205
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 5 10005 N Unknown
## 130 10130 Y Unknown
## descr.my
## 5 Please feel free to buy. All product have been thoroughly inspected, cleaned and tested to be 100%
## 130 New - Open Box. Charger included.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 5 0 0 0 0.0000000 0 0 0
## 130 0 0 0 0.8180361 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0
## 130 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 1029 11029 N Unknown
## 1077 11077 N Unknown
## descr.my
## 1029 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## 1077 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1029 0.220126 0.5801286 0 0 0 0 0
## 1077 0.220126 0.5801286 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1029 0 0 0 0 0 0
## 1077 0 0 0 0 0 0
## [1] "Category: iPad 1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 9 10009 Y iPad 1
## 13 10013 Y iPad 1
## descr.my
## 9
## 13 GOOD CONDITION. CLEAN ICLOUD. NO LOCKS. CLEAN IMEI. This tablet has been fully tested and works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 9 0.000000 0 0 0 0.0000000 0 0
## 13 0.220126 0 0 0 0.3412301 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 9 0 0.000000 0 0 0 0
## 13 0 0.340566 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 471 10471 N iPad 1
## 1202 11202 N iPad 1
## descr.my
## 471 Used Apple Ipad 64 gig 1st generation in Great working condition and 100% functional SIM card AT&T
## 1202 Used Apple Ipad 64 gig 1st generation in Great working condition and 100% functional SIM card AT&T
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 471 0.1862605 0.245439 0 0 0 0.2705847 0
## 1202 0.1862605 0.245439 0 0 0 0.2705847 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 471 0.3392152 0.2881712 0 0 0 0
## 1202 0.3392152 0.2881712 0 0 0 0
## [1] "Category: iPad 2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 1 10001 N iPad 2
## 2 10002 Y iPad 2
## descr.my
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1 0.8071287 0.0000000 0.0000000 0 0 1.172534 0.0000000
## 2 0.0000000 0.5801286 0.2923374 0 0 0.000000 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 132 10132 N iPad 2
## 2382 12384 <NA> iPad 2
## descr.my
## 132 Overall good condition. Some wear from use. Scratches/ scuffs/ nicks/ scrapes on unit housing back,
## 2382 Device is in GOOD used cosmetic condition with normal scratches & wear, engravement on the housing.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad
## 132 0.2017822 0.2658923 0.2679759 0 0.3127942 0
## 2382 0.2421386 0.3190707 0.3215711 0 0.3753531 0
## D.T.screen D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 132 0 0 0 0 0 0 0
## 2382 0 0 0 0 0 0 0
## [1] "Category: iPad 3+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 3 10003 Y iPad 3+
## 11 10011 Y iPad 3+
## descr.my
## 3
## 11 good condition, minor wear and tear on body some light scratches on screen. functions great.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 3 0.000000 0 0.0000000 0 0.0000000 0 0.0000000
## 11 0.220126 0 0.2923374 0 0.3412301 0 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 3 0.0000000 0 0 0 0 0
## 11 0.4008907 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 40 10040 N iPad 3+
## 1602 11603 Y iPad 3+
## descr.my
## 40 Item has been professionally tested and inspected. Tests show that all features work correctly. This
## 1602 Work fine iCloud lock
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 40 0 0 0 0 0 0 0
## 1602 0 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 40 0 0.4162473 0 0 0 0
## 1602 0 0.9365565 0 0 0 0
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 16 10016 N iPadAir
## 33 10033 N iPadAir
## descr.my
## 16
## 33 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 16 0 0 0 0 0.000000 0.0000000 0
## 33 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 16 0 0 0 0 0 0
## 33 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 44 10044 N iPadAir
## 1233 11233 Y iPadAir
## descr.my
## 44 Open Box Units Grade A Condition. Units may contain minor cosmetic imperfections.
## 1233 MINT CONDITION!
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 44 0.220126 0 0 0 0 0 0
## 1233 1.210693 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 44 0 0 0 0 0 0
## 1233 0 0 0 0 0 0
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 7 10007 Y iPadmini
## 76 10076 Y iPadmini
## descr.my
## 7
## 76 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 7 0.0000000 0 0 0 0.0000000 0 0
## 76 0.3026733 0 0 0 0.4691913 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 7 0 0.0000000 0 0 0 0
## 76 0 0.9365565 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 491 10491 N iPadmini
## 1753 11754 N iPadmini
## descr.my
## 491 Cracked screen, flaw is shown in picture, everything is fully functional and
## 1753 Shows Apple ID locked, password required on activation screen. Unknown imei and storage space.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 491 0 0 0 0 0 0 0.4551091
## 1753 0 0 0 0 0 0 0.4045414
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 491 0 0 0 0 0 0
## 1753 0 0 0 0 0 0
## [1] "Category: iPadmini 2+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 4 10004 N iPadmini 2+
## 18 10018 N iPadmini 2+
## descr.my
## 4
## 18 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 4 0 0 0 0 0.000000 0.0000000 0
## 18 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0
## 18 0 0 0 0 0 0
## [1] "min distance(0.0000) pair:"
## UniqueID sold.fctr prdline.my descr.my D.T.condit D.T.use D.T.scratch
## 4 10004 N iPadmini 2+ 0 0 0
## 6 10006 Y iPadmini 2+ 0 0 0
## D.T.new D.T.good D.T.ipad D.T.screen D.T.great D.T.work D.T.excel
## 4 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air
## 4 0 0 0
## 6 0 0 0
## [1] "glb_allobs_df$prdline.my$.clusterid Entropy: 0.6738 (98.3669 pct)"
## prdline.my.clusterid N Y .entropy .knt
## 1 Unknown_1 77 53 0.6760076 130
## 2 Unknown_2 26 22 0.6896709 48
## 3 Unknown_3 15 5 0.5623351 20
## 4 iPad 1_1 62 88 0.6780488 150
## 5 iPad 1_2 13 16 0.6877868 29
## 6 iPad 1_3 13 13 0.6931472 26
## 7 iPad 1_4 12 8 0.6730117 20
## 8 iPad 2_1 72 94 0.6843392 166
## 9 iPad 2_2 40 29 0.6803854 69
## 10 iPad 2_3 18 5 0.5235863 23
## 11 iPad 2_4 6 11 0.6492484 17
## 12 iPad 2_5 5 8 0.6662784 13
## 13 iPad 3+_1 85 101 0.6894428 186
## 14 iPad 3+_2 41 13 0.5519323 54
## 15 iPad 3+_3 23 16 0.6769517 39
## 16 iPad 3+_4 17 15 0.6911928 32
## 17 iPadAir_1 142 107 0.6832355 249
## 18 iPadAir_2 28 24 0.6901857 52
## 19 iPadAir_3 17 10 0.6591525 27
## 20 iPadAir_4 16 9 0.6534182 25
## 21 iPadmini 2+_1 100 61 0.6635142 161
## 22 iPadmini 2+_2 15 10 0.6730117 25
## 23 iPadmini 2+_3 10 9 0.6917615 19
## 24 iPadmini_1 99 87 0.6910646 186
## 25 iPadmini_2 17 14 0.6884572 31
## 26 iPadmini_3 11 18 0.6637255 29
## 27 iPadmini_4 12 10 0.6890092 22
## 28 iPadmini_5 7 4 0.6554818 11# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 91.469 94.675 3.206
## 8 select.features 5 0 94.676 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.0000000000
## biddable biddable 0.5481788380
## startprice.log startprice.log -0.4674275376
## startprice startprice -0.4569767211
## startprice.predict. startprice.predict. -0.3790187140
## startprice.diff startprice.diff -0.3167760893
## UniqueID UniqueID -0.1895466260
## idseq.my idseq.my -0.1895466260
## condition.fctr condition.fctr -0.1535490071
## D.npnct05.log D.npnct05.log -0.1180558939
## D.terms.n.post.stop D.terms.n.post.stop -0.0840450507
## D.terms.n.post.stem D.terms.n.post.stem -0.0838967832
## D.npnct14.log D.npnct14.log -0.0786203827
## cellular.fctr cellular.fctr -0.0739443419
## carrier.fctr carrier.fctr -0.0687945647
## D.terms.n.post.stem.log D.terms.n.post.stem.log -0.0656268732
## D.nwrds.unq.log D.nwrds.unq.log -0.0656268732
## D.terms.n.post.stop.log D.terms.n.post.stop.log -0.0656220969
## D.ndgts.log D.ndgts.log -0.0637072932
## D.npnct09.log D.npnct09.log -0.0618253281
## D.nwrds.log D.nwrds.log -0.0590061339
## D.npnct12.log D.npnct12.log -0.0568348971
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.0567372450
## D.nchrs.log D.nchrs.log -0.0565369891
## D.nuppr.log D.nuppr.log -0.0553942172
## D.npnct28.log D.npnct28.log -0.0524583244
## D.npnct06.log D.npnct06.log -0.0499761958
## D.npnct15.log D.npnct15.log 0.0484022793
## D.npnct24.log D.npnct24.log -0.0458449965
## D.nstopwrds.log D.nstopwrds.log -0.0454507052
## D.npnct16.log D.npnct16.log -0.0449403962
## color.fctr color.fctr -0.0433350320
## prdline.my.fctr prdline.my.fctr -0.0415814340
## D.npnct08.log D.npnct08.log -0.0396513123
## D.T.new D.T.new -0.0384952089
## D.npnct13.log D.npnct13.log -0.0373463069
## D.T.condit D.T.condit -0.0368137842
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -0.0360956970
## D.sum.TfIdf D.sum.TfIdf -0.0360956970
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -0.0333517826
## .clusterid .clusterid -0.0331547453
## .clusterid.fctr .clusterid.fctr -0.0331547453
## D.T.excel D.T.excel 0.0264721403
## D.npnct03.log D.npnct03.log 0.0257637868
## D.npnct07.log D.npnct07.log 0.0250040676
## D.T.screen D.T.screen 0.0246517455
## D.npnct10.log D.npnct10.log -0.0241015016
## D.npnct18.log D.npnct18.log -0.0215250231
## D.npnct11.log D.npnct11.log -0.0192035548
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.0163860117
## D.T.ipad D.T.ipad -0.0143370274
## D.T.work D.T.work -0.0128924650
## D.T.use D.T.use 0.0122706569
## D.P.mini D.P.mini -0.0112418293
## storage.fctr storage.fctr -0.0110508589
## D.P.air D.P.air -0.0092629952
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio -0.0087527853
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds 0.0078275033
## D.T.great D.T.great 0.0074871041
## .rnorm .rnorm 0.0067562740
## D.T.scratch D.T.scratch -0.0066323680
## D.npnct01.log D.npnct01.log 0.0041255300
## D.T.good D.T.good -0.0005014283
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## biddable 0 0.5481788380
## startprice.log 1 0.4674275376
## startprice 1 0.4569767211
## startprice.predict. 1 0.3790187140
## startprice.diff 0 0.3167760893
## UniqueID 1 0.1895466260
## idseq.my 0 0.1895466260
## condition.fctr 0 0.1535490071
## D.npnct05.log 0 0.1180558939
## D.terms.n.post.stop 0 0.0840450507
## D.terms.n.post.stem 0 0.0838967832
## D.npnct14.log 0 0.0786203827
## cellular.fctr 0 0.0739443419
## carrier.fctr 0 0.0687945647
## D.terms.n.post.stem.log 0 0.0656268732
## D.nwrds.unq.log 0 0.0656268732
## D.terms.n.post.stop.log 0 0.0656220969
## D.ndgts.log 0 0.0637072932
## D.npnct09.log 0 0.0618253281
## D.nwrds.log 0 0.0590061339
## D.npnct12.log 0 0.0568348971
## D.ratio.nstopwrds.nwrds 0 0.0567372450
## D.nchrs.log 0 0.0565369891
## D.nuppr.log 0 0.0553942172
## D.npnct28.log 0 0.0524583244
## D.npnct06.log 0 0.0499761958
## D.npnct15.log 0 0.0484022793
## D.npnct24.log 0 0.0458449965
## D.nstopwrds.log 0 0.0454507052
## D.npnct16.log 0 0.0449403962
## color.fctr 0 0.0433350320
## prdline.my.fctr 0 0.0415814340
## D.npnct08.log 0 0.0396513123
## D.T.new 1 0.0384952089
## D.npnct13.log 0 0.0373463069
## D.T.condit 1 0.0368137842
## D.TfIdf.sum.post.stem 0 0.0360956970
## D.sum.TfIdf 0 0.0360956970
## D.TfIdf.sum.post.stop 0 0.0333517826
## .clusterid 1 0.0331547453
## .clusterid.fctr 0 0.0331547453
## D.T.excel 1 0.0264721403
## D.npnct03.log 0 0.0257637868
## D.npnct07.log 0 0.0250040676
## D.T.screen 1 0.0246517455
## D.npnct10.log 0 0.0241015016
## D.npnct18.log 0 0.0215250231
## D.npnct11.log 0 0.0192035548
## D.terms.n.stem.stop.Ratio 0 0.0163860117
## D.T.ipad 1 0.0143370274
## D.T.work 1 0.0128924650
## D.T.use 1 0.0122706569
## D.P.mini 1 0.0112418293
## storage.fctr 0 0.0110508589
## D.P.air 1 0.0092629952
## D.TfIdf.sum.stem.stop.Ratio 0 0.0087527853
## D.ratio.sum.TfIdf.nwrds 0 0.0078275033
## D.T.great 1 0.0074871041
## .rnorm 0 0.0067562740
## D.T.scratch 1 0.0066323680
## D.npnct01.log 0 0.0041255300
## D.T.good 1 0.0005014283
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 1 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0361"
## [1] "cor(sold.fctr, D.sum.TfIdf)=-0.0361"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stem.log)=1.0000"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0656"
## [1] "cor(sold.fctr, D.terms.n.post.stem.log)=-0.0656"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop.log)=0.9999"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0656"
## [1] "cor(sold.fctr, D.terms.n.post.stop.log)=-0.0656"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9995"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0565"
## [1] "cor(sold.fctr, D.nuppr.log)=-0.0554"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.nchrs.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(sold.fctr, D.terms.n.post.stem)=-0.0839"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0840"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9977"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0361"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stop)=-0.0334"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stop as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nchrs.log, D.nwrds.unq.log)=0.9932"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0565"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0656"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.log, D.nwrds.unq.log)=0.9925"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0590"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0656"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop)=0.9752"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0656"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0840"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9641"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0458"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0567"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(sold.fctr, D.npnct06.log)=-0.0500"
## [1] "cor(sold.fctr, D.npnct16.log)=-0.0449"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.TfIdf.sum.post.stem, D.ratio.nstopwrds.nwrds)=-0.9254"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0361"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0567"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.nstopwrds.log, D.terms.n.post.stop)=0.8931"
## [1] "cor(sold.fctr, D.nstopwrds.log)=-0.0455"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0840"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.ratio.nstopwrds.nwrds, D.terms.n.post.stop)=-0.8677"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0567"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0840"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.terms.n.post.stop## [1] "cor(carrier.fctr, cellular.fctr)=0.8460"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0688"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0739"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## [1] "cor(D.npnct13.log, D.terms.n.post.stop)=0.7381"
## [1] "cor(sold.fctr, D.npnct13.log)=-0.0373"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0840"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct13.log as highly correlated with
## D.terms.n.post.stop## id cor.y exclude.as.feat cor.y.abs
## 72 sold 1.0000000000 1 1.0000000000
## 65 biddable 0.5481788380 0 0.5481788380
## 56 D.ratio.nstopwrds.nwrds 0.0567372450 0 0.0567372450
## 36 D.npnct15.log 0.0484022793 0 0.0484022793
## 8 D.T.excel 0.0264721403 1 0.0264721403
## 24 D.npnct03.log 0.0257637868 0 0.0257637868
## 28 D.npnct07.log 0.0250040676 0 0.0250040676
## 14 D.T.screen 0.0246517455 1 0.0246517455
## 63 D.terms.n.stem.stop.Ratio 0.0163860117 0 0.0163860117
## 15 D.T.use 0.0122706569 1 0.0122706569
## 57 D.ratio.sum.TfIdf.nwrds 0.0078275033 0 0.0078275033
## 10 D.T.great 0.0074871041 1 0.0074871041
## 3 .rnorm 0.0067562740 0 0.0067562740
## 22 D.npnct01.log 0.0041255300 0 0.0041255300
## 9 D.T.good -0.0005014283 1 0.0005014283
## 13 D.T.scratch -0.0066323680 1 0.0066323680
## 19 D.TfIdf.sum.stem.stop.Ratio -0.0087527853 0 0.0087527853
## 4 D.P.air -0.0092629952 1 0.0092629952
## 77 storage.fctr -0.0110508589 0 0.0110508589
## 6 D.P.mini -0.0112418293 1 0.0112418293
## 16 D.T.work -0.0128924650 1 0.0128924650
## 11 D.T.ipad -0.0143370274 1 0.0143370274
## 32 D.npnct11.log -0.0192035548 0 0.0192035548
## 39 D.npnct18.log -0.0215250231 0 0.0215250231
## 31 D.npnct10.log -0.0241015016 0 0.0241015016
## 1 .clusterid -0.0331547453 1 0.0331547453
## 2 .clusterid.fctr -0.0331547453 0 0.0331547453
## 18 D.TfIdf.sum.post.stop -0.0333517826 0 0.0333517826
## 17 D.TfIdf.sum.post.stem -0.0360956970 0 0.0360956970
## 58 D.sum.TfIdf -0.0360956970 0 0.0360956970
## 7 D.T.condit -0.0368137842 1 0.0368137842
## 34 D.npnct13.log -0.0373463069 0 0.0373463069
## 12 D.T.new -0.0384952089 1 0.0384952089
## 29 D.npnct08.log -0.0396513123 0 0.0396513123
## 71 prdline.my.fctr -0.0415814340 0 0.0415814340
## 68 color.fctr -0.0433350320 0 0.0433350320
## 37 D.npnct16.log -0.0449403962 0 0.0449403962
## 52 D.nstopwrds.log -0.0454507052 0 0.0454507052
## 45 D.npnct24.log -0.0458449965 0 0.0458449965
## 27 D.npnct06.log -0.0499761958 0 0.0499761958
## 49 D.npnct28.log -0.0524583244 0 0.0524583244
## 53 D.nuppr.log -0.0553942172 0 0.0553942172
## 20 D.nchrs.log -0.0565369891 0 0.0565369891
## 33 D.npnct12.log -0.0568348971 0 0.0568348971
## 54 D.nwrds.log -0.0590061339 0 0.0590061339
## 30 D.npnct09.log -0.0618253281 0 0.0618253281
## 21 D.ndgts.log -0.0637072932 0 0.0637072932
## 62 D.terms.n.post.stop.log -0.0656220969 0 0.0656220969
## 55 D.nwrds.unq.log -0.0656268732 0 0.0656268732
## 60 D.terms.n.post.stem.log -0.0656268732 0 0.0656268732
## 66 carrier.fctr -0.0687945647 0 0.0687945647
## 67 cellular.fctr -0.0739443419 0 0.0739443419
## 35 D.npnct14.log -0.0786203827 0 0.0786203827
## 59 D.terms.n.post.stem -0.0838967832 0 0.0838967832
## 61 D.terms.n.post.stop -0.0840450507 0 0.0840450507
## 26 D.npnct05.log -0.1180558939 0 0.1180558939
## 69 condition.fctr -0.1535490071 0 0.1535490071
## 64 UniqueID -0.1895466260 1 0.1895466260
## 70 idseq.my -0.1895466260 0 0.1895466260
## 74 startprice.diff -0.3167760893 0 0.3167760893
## 76 startprice.predict. -0.3790187140 1 0.3790187140
## 73 startprice -0.4569767211 1 0.4569767211
## 75 startprice.log -0.4674275376 1 0.4674275376
## 5 D.P.http NA 1 NA
## 23 D.npnct02.log NA 0 NA
## 25 D.npnct04.log NA 0 NA
## 38 D.npnct17.log NA 0 NA
## 40 D.npnct19.log NA 0 NA
## 41 D.npnct20.log NA 0 NA
## 42 D.npnct21.log NA 0 NA
## 43 D.npnct22.log NA 0 NA
## 44 D.npnct23.log NA 0 NA
## 46 D.npnct25.log NA 0 NA
## 47 D.npnct26.log NA 0 NA
## 48 D.npnct27.log NA 0 NA
## 50 D.npnct29.log NA 0 NA
## 51 D.npnct30.log NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 72 <NA> 1.161628 0.10758472 FALSE FALSE
## 65 <NA> 1.221027 0.10758472 FALSE FALSE
## 56 D.terms.n.post.stop 13.048780 4.24959656 FALSE FALSE
## 36 <NA> 153.416667 0.16137708 FALSE TRUE
## 8 <NA> 128.285714 0.75309306 FALSE TRUE
## 24 <NA> 83.227273 0.16137708 FALSE TRUE
## 28 <NA> 1858.000000 0.10758472 FALSE TRUE
## 14 <NA> 53.343750 0.80688542 FALSE TRUE
## 63 <NA> 77.826087 0.48413125 FALSE TRUE
## 15 <NA> 44.675676 0.91447015 FALSE TRUE
## 57 <NA> 63.000000 34.85745024 FALSE FALSE
## 10 <NA> 118.400000 0.80688542 FALSE TRUE
## 3 <NA> 1.000000 100.00000000 FALSE FALSE
## 22 <NA> 52.970588 0.32275417 FALSE TRUE
## 9 <NA> 45.315789 0.86067778 FALSE TRUE
## 13 <NA> 41.400000 0.86067778 FALSE TRUE
## 19 <NA> 65.176471 32.92092523 FALSE FALSE
## 4 <NA> 122.866667 0.16137708 FALSE TRUE
## 77 <NA> 2.733138 0.26896181 FALSE FALSE
## 6 <NA> 91.900000 0.16137708 FALSE TRUE
## 16 <NA> 59.241379 0.69930070 FALSE TRUE
## 11 <NA> 49.823529 0.80688542 FALSE TRUE
## 32 <NA> 9.374269 0.37654653 FALSE FALSE
## 39 <NA> 1858.000000 0.10758472 FALSE TRUE
## 31 <NA> 308.666667 0.16137708 FALSE TRUE
## 1 <NA> 3.987013 0.26896181 FALSE FALSE
## 2 <NA> 3.987013 0.26896181 FALSE FALSE
## 18 D.TfIdf.sum.post.stem 63.000000 34.42711135 FALSE FALSE
## 17 D.ratio.nstopwrds.nwrds 63.000000 34.31952663 FALSE FALSE
## 58 D.TfIdf.sum.post.stem 63.000000 34.31952663 FALSE FALSE
## 7 <NA> 22.984848 0.91447015 FALSE TRUE
## 34 D.terms.n.post.stop 5.203065 0.32275417 FALSE FALSE
## 12 <NA> 103.000000 0.86067778 FALSE TRUE
## 29 <NA> 69.576923 0.21516945 FALSE TRUE
## 71 <NA> 1.135048 0.37654653 FALSE FALSE
## 68 <NA> 1.574610 0.26896181 FALSE FALSE
## 37 D.npnct06.log 31.245614 0.16137708 FALSE TRUE
## 52 D.terms.n.post.stop 13.916667 0.80688542 FALSE FALSE
## 45 D.ratio.nstopwrds.nwrds 1.356147 0.10758472 FALSE FALSE
## 27 <NA> 33.735849 0.16137708 FALSE TRUE
## 49 <NA> 463.250000 0.16137708 FALSE TRUE
## 53 D.nchrs.log 18.807018 4.41097364 FALSE FALSE
## 20 D.nwrds.unq.log 15.970149 5.70199032 FALSE FALSE
## 33 <NA> 26.818182 0.21516945 FALSE TRUE
## 54 D.nwrds.unq.log 12.891566 1.29101668 FALSE FALSE
## 30 <NA> 308.333333 0.21516945 FALSE TRUE
## 21 <NA> 27.031746 0.69930070 FALSE TRUE
## 62 D.nwrds.unq.log 8.052632 0.80688542 FALSE FALSE
## 55 D.terms.n.post.stop 7.595745 0.80688542 FALSE FALSE
## 60 D.nwrds.unq.log 7.595745 0.80688542 FALSE FALSE
## 66 cellular.fctr 3.195965 0.37654653 FALSE FALSE
## 67 <NA> 2.112381 0.16137708 FALSE FALSE
## 35 <NA> 35.333333 0.26896181 FALSE TRUE
## 59 D.terms.n.post.stop 7.595745 0.80688542 FALSE FALSE
## 61 <NA> 8.052632 0.80688542 FALSE FALSE
## 26 <NA> 40.311111 0.10758472 FALSE TRUE
## 69 <NA> 4.003460 0.32275417 FALSE FALSE
## 64 <NA> 1.000000 100.00000000 FALSE FALSE
## 70 <NA> 1.000000 100.00000000 FALSE FALSE
## 74 <NA> 1.000000 99.89241528 FALSE FALSE
## 76 <NA> 1.333333 99.03173749 FALSE FALSE
## 73 <NA> 2.807692 30.17751479 FALSE FALSE
## 75 <NA> 2.807692 30.17751479 FALSE FALSE
## 5 <NA> 0.000000 0.05379236 TRUE TRUE
## 23 <NA> 0.000000 0.05379236 TRUE TRUE
## 25 <NA> 0.000000 0.05379236 TRUE TRUE
## 38 <NA> 0.000000 0.05379236 TRUE TRUE
## 40 <NA> 0.000000 0.05379236 TRUE TRUE
## 41 <NA> 0.000000 0.05379236 TRUE TRUE
## 42 <NA> 0.000000 0.05379236 TRUE TRUE
## 43 <NA> 0.000000 0.05379236 TRUE TRUE
## 44 <NA> 0.000000 0.05379236 TRUE TRUE
## 46 <NA> 0.000000 0.05379236 TRUE TRUE
## 47 <NA> 0.000000 0.05379236 TRUE TRUE
## 48 <NA> 0.000000 0.05379236 TRUE TRUE
## 50 <NA> 0.000000 0.05379236 TRUE TRUE
## 51 <NA> 0.000000 0.05379236 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 72 FALSE FALSE
## 65 FALSE FALSE
## 56 FALSE FALSE
## 36 FALSE FALSE
## 8 FALSE FALSE
## 24 FALSE FALSE
## 28 TRUE FALSE
## 14 FALSE FALSE
## 63 FALSE FALSE
## 15 FALSE FALSE
## 57 FALSE FALSE
## 10 FALSE FALSE
## 3 FALSE FALSE
## 22 FALSE TRUE
## 9 FALSE TRUE
## 13 FALSE TRUE
## 19 FALSE FALSE
## 4 FALSE FALSE
## 77 FALSE FALSE
## 6 FALSE FALSE
## 16 FALSE FALSE
## 11 FALSE FALSE
## 32 FALSE FALSE
## 39 TRUE FALSE
## 31 FALSE FALSE
## 1 FALSE FALSE
## 2 FALSE FALSE
## 18 FALSE FALSE
## 17 FALSE FALSE
## 58 FALSE FALSE
## 7 FALSE FALSE
## 34 FALSE FALSE
## 12 FALSE FALSE
## 29 FALSE FALSE
## 71 FALSE FALSE
## 68 FALSE FALSE
## 37 FALSE FALSE
## 52 FALSE FALSE
## 45 FALSE FALSE
## 27 FALSE FALSE
## 49 FALSE FALSE
## 53 FALSE FALSE
## 20 FALSE FALSE
## 33 FALSE FALSE
## 54 FALSE FALSE
## 30 FALSE FALSE
## 21 FALSE FALSE
## 62 FALSE FALSE
## 55 FALSE FALSE
## 60 FALSE FALSE
## 66 FALSE FALSE
## 67 FALSE FALSE
## 35 FALSE FALSE
## 59 FALSE FALSE
## 61 FALSE FALSE
## 26 FALSE FALSE
## 69 FALSE FALSE
## 64 FALSE FALSE
## 70 FALSE FALSE
## 74 FALSE FALSE
## 76 FALSE FALSE
## 73 FALSE FALSE
## 75 FALSE FALSE
## 5 TRUE NA
## 23 TRUE NA
## 25 TRUE NA
## 38 TRUE NA
## 40 TRUE NA
## 41 TRUE NA
## 42 TRUE NA
## 43 TRUE NA
## 44 TRUE NA
## 46 TRUE NA
## 47 TRUE NA
## 48 TRUE NA
## 50 TRUE NA
## 51 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 12 rows containing missing values (geom_point).## Warning: Removed 12 rows containing missing values (geom_point).## Warning: Removed 12 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 28 D.npnct07.log 0.02500407 0 0.02500407 <NA>
## 39 D.npnct18.log -0.02152502 0 0.02152502 <NA>
## 5 D.P.http NA 1 NA <NA>
## 23 D.npnct02.log NA 0 NA <NA>
## 25 D.npnct04.log NA 0 NA <NA>
## 38 D.npnct17.log NA 0 NA <NA>
## 40 D.npnct19.log NA 0 NA <NA>
## 41 D.npnct20.log NA 0 NA <NA>
## 42 D.npnct21.log NA 0 NA <NA>
## 43 D.npnct22.log NA 0 NA <NA>
## 44 D.npnct23.log NA 0 NA <NA>
## 46 D.npnct25.log NA 0 NA <NA>
## 47 D.npnct26.log NA 0 NA <NA>
## 48 D.npnct27.log NA 0 NA <NA>
## 50 D.npnct29.log NA 0 NA <NA>
## 51 D.npnct30.log NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 28 1858 0.10758472 FALSE TRUE TRUE FALSE
## 39 1858 0.10758472 FALSE TRUE TRUE FALSE
## 5 0 0.05379236 TRUE TRUE TRUE NA
## 23 0 0.05379236 TRUE TRUE TRUE NA
## 25 0 0.05379236 TRUE TRUE TRUE NA
## 38 0 0.05379236 TRUE TRUE TRUE NA
## 40 0 0.05379236 TRUE TRUE TRUE NA
## 41 0 0.05379236 TRUE TRUE TRUE NA
## 42 0 0.05379236 TRUE TRUE TRUE NA
## 43 0 0.05379236 TRUE TRUE TRUE NA
## 44 0 0.05379236 TRUE TRUE TRUE NA
## 46 0 0.05379236 TRUE TRUE TRUE NA
## 47 0 0.05379236 TRUE TRUE TRUE NA
## 48 0 0.05379236 TRUE TRUE TRUE NA
## 50 0 0.05379236 TRUE TRUE TRUE NA
## 51 0 0.05379236 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct03.log D.npnct05.log
## 2579 2614 2592
## D.npnct06.log D.npnct08.log D.npnct09.log
## 2554 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct24.log D.npnct28.log
## 2546 1520 2649
## D.nstopwrds.log D.P.mini D.P.air
## 1663 2623 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 94.676 98.481 3.805
## 9 partition.data.training 6 0 98.481 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 798
## Fit 524 450 NA
## OOB 475 410 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.5379877 0.4620123 NA
## OOB 0.5367232 0.4632768 NA
## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 154 171 0.1929825 0.1932203
## 5 iPadAir 137 151 0.1716792 0.1706215
## 4 iPad 3+ 123 136 0.1541353 0.1536723
## 6 iPadmini 114 127 0.1428571 0.1435028
## 7 iPadmini 2+ 94 104 0.1177945 0.1175141
## 2 iPad 1 89 99 0.1115288 0.1118644
## 1 Unknown 87 97 0.1090226 0.1096045# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 77 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 72 sold 1.0000000 TRUE 1.0000000 <NA>
## 64 UniqueID -0.1895466 TRUE 0.1895466 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 72 1.161628 0.1075847 FALSE FALSE FALSE FALSE
## 64 1.000000 100.0000000 FALSE FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 72 <NA> TRUE NA NA
## 64 <NA> FALSE TRUE NA
## sold.fctr <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct07.log" "D.npnct18.log" "D.P.http" "D.npnct02.log"
## [5] "D.npnct04.log" "D.npnct17.log" "D.npnct19.log" "D.npnct20.log"
## [9] "D.npnct21.log" "D.npnct22.log" "D.npnct23.log" "D.npnct25.log"
## [13] "D.npnct26.log" "D.npnct27.log" "D.npnct29.log" "D.npnct30.log"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2657 74print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1859 73print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 974 73print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 885 73print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 73# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 98.481 99.426 0.945
## 10 fit.models 7 0 99.427 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5379877 0.4620123
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379877 0.4620123
## 2 0.5379877 0.4620123
## 3 0.5379877 0.4620123
## 4 0.5379877 0.4620123
## 5 0.5379877 0.4620123
## 6 0.5379877 0.4620123
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 524 0
## Y 450 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379877e-01 0.000000e+00 5.060896e-01 5.696555e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.131410e-01 1.962860e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379877 0.4620123
## 2 0.5379877 0.4620123
## 3 0.5379877 0.4620123
## 4 0.5379877 0.4620123
## 5 0.5379877 0.4620123
## 6 0.5379877 0.4620123
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 475 0
## Y 410 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.367232e-01 0.000000e+00 5.032294e-01 5.699717e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.137716e-01 9.975777e-91
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.417 0.003 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5379877
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060896 0.5696555 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5367232
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5032294 0.5699717 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.6320225
## 5 0.4 0.6320225
## 6 0.5 0.4747253
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 0 524
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.620123e-01 0.000000e+00 4.303445e-01 4.939104e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 9.999991e-01 1.552393e-115
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.6332046
## 5 0.4 0.6332046
## 6 0.5 0.4822521
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 0 475
## Y 0 410
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.632768e-01 0.000000e+00 4.300283e-01 4.967706e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 9.999948e-01 7.120214e-105
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.282 0.002 0.5071756
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.6320225 0.4620123
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.4303445 0.4939104 0 0.5191913
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6332046 0.4632768
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4300283 0.4967706 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, startprice.diff"## Loading required package: rpart## Fitting cp = 0.511 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.5111111 0 1
##
## Node number 1: 974 observations
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.5379877 0.4620123) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 524 0
## Y 450 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379877e-01 0.000000e+00 5.060896e-01 5.696555e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.131410e-01 1.962860e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 475 0
## Y 410 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.367232e-01 0.000000e+00 5.032294e-01 5.699717e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.137716e-01 9.975777e-91
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, startprice.diff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.634 0.012
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5379877
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060896 0.5696555 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5367232
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5032294 0.5699717 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, startprice.diff"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.148888889 1 0.4888889
## 3 0.005333333 2 0.3400000
## 4 0.001481481 11 0.2844444
## 5 0.000000000 14 0.2800000
##
## Variable importance
## biddable startprice.diff
## 51 49
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 44.18097 to the right, improve= 78.49785, (0 missing)
## Surrogate splits:
## startprice.diff < 244.0148 to the left, agree=0.548, adj=0.022, (0 split)
##
## Node number 2: 524 observations, complexity param=0.005333333
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
## left son=4 (241 obs) right son=5 (283 obs)
## Primary splits:
## startprice.diff < 18.6704 to the right, improve=11.698, (0 missing)
##
## Node number 3: 450 observations, complexity param=0.1488889
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (89 obs) right son=7 (361 obs)
## Primary splits:
## startprice.diff < 84.72197 to the right, improve=88.61445, (0 missing)
##
## Node number 4: 241 observations
## predicted class=N expected loss=0.09543568 P(node) =0.2474333
## class counts: 218 23
## probabilities: 0.905 0.095
##
## Node number 5: 283 observations, complexity param=0.005333333
## predicted class=N expected loss=0.3074205 P(node) =0.2905544
## class counts: 196 87
## probabilities: 0.693 0.307
## left son=10 (128 obs) right son=11 (155 obs)
## Primary splits:
## startprice.diff < -16.96598 to the left, improve=9.605911, (0 missing)
##
## Node number 6: 89 observations
## predicted class=N expected loss=0.1235955 P(node) =0.09137577
## class counts: 78 11
## probabilities: 0.876 0.124
##
## Node number 7: 361 observations, complexity param=0.001481481
## predicted class=Y expected loss=0.08864266 P(node) =0.3706366
## class counts: 32 329
## probabilities: 0.089 0.911
## left son=14 (47 obs) right son=15 (314 obs)
## Primary splits:
## startprice.diff < 41.70308 to the right, improve=8.057863, (0 missing)
##
## Node number 10: 128 observations
## predicted class=N expected loss=0.1640625 P(node) =0.1314168
## class counts: 107 21
## probabilities: 0.836 0.164
##
## Node number 11: 155 observations, complexity param=0.005333333
## predicted class=N expected loss=0.4258065 P(node) =0.1591376
## class counts: 89 66
## probabilities: 0.574 0.426
## left son=22 (49 obs) right son=23 (106 obs)
## Primary splits:
## startprice.diff < 5.201847 to the right, improve=5.807796, (0 missing)
##
## Node number 14: 47 observations, complexity param=0.001481481
## predicted class=Y expected loss=0.3617021 P(node) =0.04825462
## class counts: 17 30
## probabilities: 0.362 0.638
## left son=28 (40 obs) right son=29 (7 obs)
## Primary splits:
## startprice.diff < 76.69886 to the left, improve=0.7878419, (0 missing)
##
## Node number 15: 314 observations
## predicted class=Y expected loss=0.0477707 P(node) =0.3223819
## class counts: 15 299
## probabilities: 0.048 0.952
##
## Node number 22: 49 observations
## predicted class=N expected loss=0.2244898 P(node) =0.05030801
## class counts: 38 11
## probabilities: 0.776 0.224
##
## Node number 23: 106 observations, complexity param=0.005333333
## predicted class=Y expected loss=0.4811321 P(node) =0.1088296
## class counts: 51 55
## probabilities: 0.481 0.519
## left son=46 (94 obs) right son=47 (12 obs)
## Primary splits:
## startprice.diff < 2.233528 to the left, improve=1.445805, (0 missing)
##
## Node number 28: 40 observations, complexity param=0.001481481
## predicted class=Y expected loss=0.4 P(node) =0.04106776
## class counts: 16 24
## probabilities: 0.400 0.600
## left son=56 (10 obs) right son=57 (30 obs)
## Primary splits:
## startprice.diff < 66.78261 to the right, improve=1.066667, (0 missing)
##
## Node number 29: 7 observations
## predicted class=Y expected loss=0.1428571 P(node) =0.007186858
## class counts: 1 6
## probabilities: 0.143 0.857
##
## Node number 46: 94 observations, complexity param=0.005333333
## predicted class=N expected loss=0.4893617 P(node) =0.09650924
## class counts: 48 46
## probabilities: 0.511 0.489
## left son=92 (80 obs) right son=93 (14 obs)
## Primary splits:
## startprice.diff < -13.80023 to the right, improve=1.664438, (0 missing)
##
## Node number 47: 12 observations
## predicted class=Y expected loss=0.25 P(node) =0.01232033
## class counts: 3 9
## probabilities: 0.250 0.750
##
## Node number 56: 10 observations
## predicted class=N expected loss=0.4 P(node) =0.01026694
## class counts: 6 4
## probabilities: 0.600 0.400
##
## Node number 57: 30 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.03080082
## class counts: 10 20
## probabilities: 0.333 0.667
##
## Node number 92: 80 observations, complexity param=0.005333333
## predicted class=N expected loss=0.45 P(node) =0.08213552
## class counts: 44 36
## probabilities: 0.550 0.450
## left son=184 (9 obs) right son=185 (71 obs)
## Primary splits:
## startprice.diff < 0.8402056 to the right, improve=1.052269, (0 missing)
##
## Node number 93: 14 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.01437372
## class counts: 4 10
## probabilities: 0.286 0.714
##
## Node number 184: 9 observations
## predicted class=N expected loss=0.2222222 P(node) =0.009240246
## class counts: 7 2
## probabilities: 0.778 0.222
##
## Node number 185: 71 observations, complexity param=0.005333333
## predicted class=N expected loss=0.4788732 P(node) =0.07289528
## class counts: 37 34
## probabilities: 0.521 0.479
## left son=370 (48 obs) right son=371 (23 obs)
## Primary splits:
## startprice.diff < -3.169162 to the left, improve=2.043504, (0 missing)
##
## Node number 370: 48 observations, complexity param=0.005333333
## predicted class=N expected loss=0.3958333 P(node) =0.04928131
## class counts: 29 19
## probabilities: 0.604 0.396
## left son=740 (19 obs) right son=741 (29 obs)
## Primary splits:
## startprice.diff < -7.269784 to the right, improve=1.107154, (0 missing)
##
## Node number 371: 23 observations
## predicted class=Y expected loss=0.3478261 P(node) =0.02361396
## class counts: 8 15
## probabilities: 0.348 0.652
##
## Node number 740: 19 observations
## predicted class=N expected loss=0.2631579 P(node) =0.01950719
## class counts: 14 5
## probabilities: 0.737 0.263
##
## Node number 741: 29 observations, complexity param=0.005333333
## predicted class=N expected loss=0.4827586 P(node) =0.02977413
## class counts: 15 14
## probabilities: 0.517 0.483
## left son=1482 (17 obs) right son=1483 (12 obs)
## Primary splits:
## startprice.diff < -9.86028 to the left, improve=2.923935, (0 missing)
##
## Node number 1482: 17 observations
## predicted class=N expected loss=0.2941176 P(node) =0.0174538
## class counts: 12 5
## probabilities: 0.706 0.294
##
## Node number 1483: 12 observations
## predicted class=Y expected loss=0.25 P(node) =0.01232033
## class counts: 3 9
## probabilities: 0.250 0.750
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366)
## 4) startprice.diff>=18.6704 241 23 N (0.90456432 0.09543568) *
## 5) startprice.diff< 18.6704 283 87 N (0.69257951 0.30742049)
## 10) startprice.diff< -16.96598 128 21 N (0.83593750 0.16406250) *
## 11) startprice.diff>=-16.96598 155 66 N (0.57419355 0.42580645)
## 22) startprice.diff>=5.201847 49 11 N (0.77551020 0.22448980) *
## 23) startprice.diff< 5.201847 106 51 Y (0.48113208 0.51886792)
## 46) startprice.diff< 2.233528 94 46 N (0.51063830 0.48936170)
## 92) startprice.diff>=-13.80023 80 36 N (0.55000000 0.45000000)
## 184) startprice.diff>=0.8402056 9 2 N (0.77777778 0.22222222) *
## 185) startprice.diff< 0.8402056 71 34 N (0.52112676 0.47887324)
## 370) startprice.diff< -3.169162 48 19 N (0.60416667 0.39583333)
## 740) startprice.diff>=-7.269784 19 5 N (0.73684211 0.26315789) *
## 741) startprice.diff< -7.269784 29 14 N (0.51724138 0.48275862)
## 1482) startprice.diff< -9.86028 17 5 N (0.70588235 0.29411765) *
## 1483) startprice.diff>=-9.86028 12 3 Y (0.25000000 0.75000000) *
## 371) startprice.diff>=-3.169162 23 8 Y (0.34782609 0.65217391) *
## 93) startprice.diff< -13.80023 14 4 Y (0.28571429 0.71428571) *
## 47) startprice.diff>=2.233528 12 3 Y (0.25000000 0.75000000) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=84.72197 89 11 N (0.87640449 0.12359551) *
## 7) startprice.diff< 84.72197 361 32 Y (0.08864266 0.91135734)
## 14) startprice.diff>=41.70308 47 17 Y (0.36170213 0.63829787)
## 28) startprice.diff< 76.69886 40 16 Y (0.40000000 0.60000000)
## 56) startprice.diff>=66.78261 10 4 N (0.60000000 0.40000000) *
## 57) startprice.diff< 66.78261 30 10 Y (0.33333333 0.66666667) *
## 29) startprice.diff>=76.69886 7 1 Y (0.14285714 0.85714286) *
## 15) startprice.diff< 41.70308 314 15 Y (0.04777070 0.95222930) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7218935
## 3 0.2 0.8178054
## 4 0.3 0.8532110
## 5 0.4 0.8532110
## 6 0.5 0.8538283
## 7 0.6 0.8538283
## 8 0.7 0.8232386
## 9 0.8 0.7911803
## 10 0.9 0.7827225
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 480
## 2 Y 82
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 44
## 2 368
## Prediction
## Reference N Y
## N 480 44
## Y 82 368
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.706366e-01 7.382100e-01 8.479228e-01 8.910807e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.081623e-109 9.799455e-04
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7220149
## 3 0.2 0.7877412
## 4 0.3 0.7984791
## 5 0.4 0.7984791
## 6 0.5 0.7994859
## 7 0.6 0.7994859
## 8 0.7 0.7732240
## 9 0.8 0.7747489
## 10 0.9 0.7734488
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 418
## 2 Y 99
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 57
## 2 311
## Prediction
## Reference N Y
## N 418 57
## Y 99 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.237288e-01 6.430437e-01 7.969937e-01 8.482877e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 3.292368e-72 1.028446e-03
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, startprice.diff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.48 0.009
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9051018 0.6 0.8538283 0.8706366
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8479228 0.8910807 0.73821 0.8812811
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7994859 0.8237288
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7969937 0.8482877 0.6430437if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, startprice.diff"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00533 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.148888889 1 0.4888889
## 3 0.005333333 2 0.3400000
##
## Variable importance
## biddable startprice.diff
## 61 39
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 44.18097 to the right, improve= 78.49785, (0 missing)
## Surrogate splits:
## startprice.diff < 244.0148 to the left, agree=0.548, adj=0.022, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.1488889
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (89 obs) right son=7 (361 obs)
## Primary splits:
## startprice.diff < 84.72197 to the right, improve=88.61445, (0 missing)
##
## Node number 6: 89 observations
## predicted class=N expected loss=0.1235955 P(node) =0.09137577
## class counts: 78 11
## probabilities: 0.876 0.124
##
## Node number 7: 361 observations
## predicted class=Y expected loss=0.08864266 P(node) =0.3706366
## class counts: 32 329
## probabilities: 0.089 0.911
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=84.72197 89 11 N (0.87640449 0.12359551) *
## 7) startprice.diff< 84.72197 361 32 Y (0.08864266 0.91135734) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6576779
## 4 0.3 0.8113440
## 5 0.4 0.8113440
## 6 0.5 0.8113440
## 7 0.6 0.8113440
## 8 0.7 0.8113440
## 9 0.8 0.8113440
## 10 0.9 0.8113440
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 492
## 2 Y 121
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 32
## 2 329
## Prediction
## Reference N Y
## N 492 32
## Y 121 329
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.429158e-01 6.795322e-01 8.185193e-01 8.652175e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.699210e-90 1.124184e-12
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6515397
## 4 0.3 0.7934783
## 5 0.4 0.7934783
## 6 0.5 0.7934783
## 7 0.6 0.7934783
## 8 0.7 0.7934783
## 9 0.8 0.7934783
## 10 0.9 0.7934783
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 441
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 34
## 2 292
## Prediction
## Reference N Y
## N 441 34
## Y 118 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.282486e-01 6.497240e-01 8.017531e-01 8.525367e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.168374e-74 1.671294e-11
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, startprice.diff 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.057 0.011 0.8435581
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9 0.811344 0.8367648
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8185193 0.8652175 0.6685851 0.8273068
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.7934783 0.8282486
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8017531 0.8525367 0.649724
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01585287 0.03187391# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.diff"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5037 -0.6915 -0.2142 0.5728 2.4797
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.240824 0.115783 -10.72 <2e-16 ***
## biddable 3.061046 0.187265 16.35 <2e-16 ***
## startprice.diff -0.013661 0.001273 -10.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 862.31 on 971 degrees of freedom
## AIC: 868.31
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6863354
## 3 0.2 0.7383260
## 4 0.3 0.7338877
## 5 0.4 0.7569367
## 6 0.5 0.7710012
## 7 0.6 0.7872340
## 8 0.7 0.7906404
## 9 0.8 0.7675963
## 10 0.9 0.2407767
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 483
## 2 Y 129
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 41
## 2 321
## Prediction
## Reference N Y
## N 483 41
## Y 129 321
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.254620e-01 6.439814e-01 8.001376e-01 8.488005e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.085595e-79 2.513186e-11
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6916525
## 3 0.2 0.7364341
## 4 0.3 0.7378190
## 5 0.4 0.7564895
## 6 0.5 0.7804878
## 7 0.6 0.7867550
## 8 0.7 0.7896175
## 9 0.8 0.7369985
## 10 0.9 0.2738589
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 442
## 2 Y 121
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 33
## 2 289
## Prediction
## Reference N Y
## N 442 33
## Y 121 289
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.259887e-01 6.448747e-01 7.993726e-01 8.504130e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.992247e-73 2.371969e-12
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm biddable, startprice.diff 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.971 0.013 0.8706955
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.7906404 0.7977525
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8001376 0.8488005 0.5911469 0.8658999
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.7896175 0.8259887
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7993726 0.850413 0.6448747 868.3141
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009555267 0.01958033if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5542 -0.6949 -0.2137 0.5631 2.4811
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.241030 0.115820 -10.715 < 2e-16
## biddable 4.453510 1.409049 3.161 0.00157
## startprice.diff -0.013687 0.001286 -10.640 < 2e-16
## `biddable:D.terms.n.post.stop` 0.104993 0.288792 0.364 0.71619
## `biddable:D.TfIdf.sum.post.stem` -0.005976 0.193806 -0.031 0.97540
## `biddable:D.ratio.nstopwrds.nwrds` -1.275311 1.390968 -0.917 0.35922
## `biddable:D.npnct06.log` -0.117525 0.879725 -0.134 0.89372
## `biddable:D.nchrs.log` 0.157820 0.900923 0.175 0.86094
## `biddable:D.nwrds.unq.log` -1.042750 2.906364 -0.359 0.71976
## `biddable:cellular.fctr1` 0.013772 0.317458 0.043 0.96540
## `biddable:cellular.fctrUnknown` -0.905962 0.366723 -2.470 0.01350
##
## (Intercept) ***
## biddable **
## startprice.diff ***
## `biddable:D.terms.n.post.stop`
## `biddable:D.TfIdf.sum.post.stem`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.npnct06.log`
## `biddable:D.nchrs.log`
## `biddable:D.nwrds.unq.log`
## `biddable:cellular.fctr1`
## `biddable:cellular.fctrUnknown` *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 855.46 on 963 degrees of freedom
## AIC: 877.46
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6868687
## 3 0.2 0.7359155
## 4 0.3 0.7343096
## 5 0.4 0.7577778
## 6 0.5 0.7727797
## 7 0.6 0.7867299
## 8 0.7 0.7915633
## 9 0.8 0.7469553
## 10 0.9 0.3357934
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 487
## 2 Y 131
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 37
## 2 319
## Prediction
## Reference N Y
## N 487 37
## Y 131 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.275154e-01 6.478336e-01 8.022954e-01 8.507367e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 6.487999e-81 7.225130e-13
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6934244
## 3 0.2 0.7357212
## 4 0.3 0.7361111
## 5 0.4 0.7530864
## 6 0.5 0.7793548
## 7 0.6 0.7862797
## 8 0.7 0.7868852
## 9 0.8 0.7321429
## 10 0.9 0.3564356
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 441
## 2 Y 122
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 34
## 2 288
## Prediction
## Reference N Y
## N 441 34
## Y 122 288
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.237288e-01 6.402627e-01 7.969937e-01 8.482877e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 3.292368e-72 3.270658e-12
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.05 0.015
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8716794 0.7 0.7915633 0.7936467
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8022954 0.8507367 0.5830988 0.8652888
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.7868852 0.8237288
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7969937 0.8482877 0.6402627 877.4626
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01031335 0.02147437# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.89910 -0.59239 -0.07592 0.47930 2.70833
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 4.122e+00 5.798e+00
## biddable 3.622e+00 2.542e-01
## D.npnct15.log 1.492e+00 8.293e-01
## D.npnct03.log -1.122e-01 1.768e+00
## D.terms.n.stem.stop.Ratio -3.245e+00 5.161e+00
## D.ratio.sum.TfIdf.nwrds 1.915e-02 1.599e-01
## .rnorm 6.985e-02 9.892e-02
## D.npnct01.log 4.453e-01 5.780e-01
## D.TfIdf.sum.stem.stop.Ratio -1.885e+00 3.109e+00
## storage.fctr16 -1.063e+00 5.572e-01
## storage.fctr32 -1.360e+00 5.893e-01
## storage.fctr64 -3.708e-01 5.798e-01
## storage.fctrUnknown -1.572e+00 7.351e-01
## D.npnct11.log -6.069e-03 3.512e-01
## D.npnct10.log -2.363e+01 1.344e+03
## D.npnct08.log 5.628e-01 6.867e-01
## `prdline.my.fctriPad 1` 1.268e+00 5.920e-01
## `prdline.my.fctriPad 2` 5.860e-01 5.897e-01
## `prdline.my.fctriPad 3+` 1.034e+00 5.655e-01
## prdline.my.fctriPadAir 1.008e+00 5.642e-01
## prdline.my.fctriPadmini 8.086e-01 5.527e-01
## `prdline.my.fctriPadmini 2+` 9.390e-01 5.951e-01
## color.fctrBlack 9.617e-02 2.669e-01
## color.fctrGold 1.636e-01 5.539e-01
## `color.fctrSpace Gray` -4.397e-01 3.630e-01
## color.fctrWhite -3.523e-01 2.634e-01
## D.npnct06.log -2.066e+00 1.275e+00
## D.npnct28.log -2.079e+00 1.720e+03
## D.npnct12.log -1.090e-01 7.129e-01
## D.npnct09.log -8.845e+00 7.670e+02
## D.ndgts.log 8.584e-01 4.531e-01
## cellular.fctr1 2.113e-01 2.391e-01
## cellular.fctrUnknown -4.450e-01 4.803e-01
## D.npnct14.log -2.088e+00 1.034e+00
## D.terms.n.post.stop -5.992e-02 4.828e-02
## D.npnct05.log -4.646e+00 1.724e+00
## `condition.fctrFor parts or not working` 5.820e-02 3.851e-01
## `condition.fctrManufacturer refurbished` 7.966e-01 6.620e-01
## condition.fctrNew -1.161e-01 3.242e-01
## `condition.fctrNew other (see details)` 8.446e-01 4.988e-01
## `condition.fctrSeller refurbished` -5.441e-01 4.277e-01
## idseq.my -5.507e-05 2.153e-04
## startprice.diff -1.823e-02 1.702e-03
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.400e+00 7.327e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 6.714e-01 8.318e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 3.358e-01 7.168e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -7.029e-01 7.963e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -8.283e-01 7.271e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.379e+00 7.615e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 5.512e-01 9.556e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.466e-01 9.062e-01
## `prdline.my.fctriPad 1:.clusterid.fctr3` -4.315e-02 9.864e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.487e+01 7.639e+02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 8.067e-02 7.007e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 6.348e-01 9.685e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 9.981e-01 8.703e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 2.058e-01 9.544e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.773e+00 8.934e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 8.574e-01 1.201e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 9.964e-01 8.034e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -4.034e-01 8.753e-01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -5.871e-01 9.413e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.841e+00 1.538e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -5.363e-01 1.329e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## z value Pr(>|z|)
## (Intercept) 0.711 0.47714
## biddable 14.250 < 2e-16 ***
## D.npnct15.log 1.799 0.07205 .
## D.npnct03.log -0.063 0.94941
## D.terms.n.stem.stop.Ratio -0.629 0.52948
## D.ratio.sum.TfIdf.nwrds 0.120 0.90469
## .rnorm 0.706 0.48009
## D.npnct01.log 0.770 0.44108
## D.TfIdf.sum.stem.stop.Ratio -0.606 0.54421
## storage.fctr16 -1.907 0.05651 .
## storage.fctr32 -2.308 0.02100 *
## storage.fctr64 -0.640 0.52247
## storage.fctrUnknown -2.139 0.03245 *
## D.npnct11.log -0.017 0.98621
## D.npnct10.log -0.018 0.98598
## D.npnct08.log 0.820 0.41247
## `prdline.my.fctriPad 1` 2.141 0.03224 *
## `prdline.my.fctriPad 2` 0.994 0.32035
## `prdline.my.fctriPad 3+` 1.828 0.06748 .
## prdline.my.fctriPadAir 1.787 0.07399 .
## prdline.my.fctriPadmini 1.463 0.14348
## `prdline.my.fctriPadmini 2+` 1.578 0.11461
## color.fctrBlack 0.360 0.71855
## color.fctrGold 0.295 0.76775
## `color.fctrSpace Gray` -1.211 0.22583
## color.fctrWhite -1.337 0.18112
## D.npnct06.log -1.620 0.10513
## D.npnct28.log -0.001 0.99904
## D.npnct12.log -0.153 0.87846
## D.npnct09.log -0.012 0.99080
## D.ndgts.log 1.894 0.05817 .
## cellular.fctr1 0.884 0.37679
## cellular.fctrUnknown -0.926 0.35419
## D.npnct14.log -2.021 0.04333 *
## D.terms.n.post.stop -1.241 0.21459
## D.npnct05.log -2.695 0.00703 **
## `condition.fctrFor parts or not working` 0.151 0.87986
## `condition.fctrManufacturer refurbished` 1.203 0.22885
## condition.fctrNew -0.358 0.72030
## `condition.fctrNew other (see details)` 1.693 0.09043 .
## `condition.fctrSeller refurbished` -1.272 0.20334
## idseq.my -0.256 0.79814
## startprice.diff -10.706 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.276 0.00105 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.807 0.41961
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.468 0.63947
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.883 0.37744
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.139 0.25468
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.811 0.07007 .
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.577 0.56406
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.162 0.87147
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.044 0.96511
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.019 0.98447
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.115 0.90833
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.655 0.51216
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.147 0.25146
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.216 0.82925
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.985 0.04720 *
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.714 0.47546
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.240 0.21486
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.461 0.64489
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.624 0.53280
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.497 0.01252 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.403 0.68667
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 736.82 on 910 degrees of freedom
## AIC: 864.82
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7302905
## 3 0.2 0.7699443
## 4 0.3 0.7915408
## 5 0.4 0.8061336
## 6 0.5 0.8123570
## 7 0.6 0.8090692
## 8 0.7 0.7950000
## 9 0.8 0.7483176
## 10 0.9 0.5776398
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 455
## 2 Y 95
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 69
## 2 355
## Prediction
## Reference N Y
## N 455 69
## Y 95 355
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.316222e-01 6.599017e-01 8.066148e-01 8.546053e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.125901e-83 5.091778e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7118959
## 3 0.2 0.7371728
## 4 0.3 0.7525892
## 5 0.4 0.7686659
## 6 0.5 0.7713921
## 7 0.6 0.7745358
## 8 0.7 0.7765668
## 9 0.8 0.7251462
## 10 0.9 0.5860927
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 436
## 2 Y 125
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 39
## 2 285
## Prediction
## Reference N Y
## N 436 39
## Y 125 285
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.146893e-01 6.219426e-01 7.874927e-01 8.397715e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.806781e-67 3.192972e-11
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.386 0.168
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9063401 0.5 0.812357 0.7854194
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8066148 0.8546053 0.5673801 0.8499512
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.7765668 0.8146893
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7874927 0.8397715 0.6219426 864.8173
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004787468 0.01254642rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 99.427 125.587 26.16
## 11 fit.models 7 1 125.587 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 129.488 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
for (model_id_pfx in c("All.X", "All.Interact.X")) {
#model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
if (model_id_pfx == "All.Interact.X") {
interact_vars_vctr <- c(
#"startprice.log",
"startprice.diff",
"biddable", "idseq.my")
indep_vars_vctr <- union(setdiff(indep_vars_vctr, interact_vars_vctr),
paste(glb_category_var, interact_vars_vctr, sep=".fctr*"))
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 129.488 129.497 0.009
## 2 fit.models_1_glm 2 0 129.497 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.05403 -0.56812 -0.05485 0.46862 2.74437
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -2.787e+03 3.374e+03
## biddable 3.725e+00 2.649e-01
## D.ratio.nstopwrds.nwrds -1.434e+01 7.540e+00
## D.npnct15.log 8.591e-01 9.183e-01
## D.npnct03.log 2.848e-01 2.079e+00
## D.terms.n.stem.stop.Ratio 2.812e+03 3.374e+03
## D.ratio.sum.TfIdf.nwrds -3.338e-01 6.613e-01
## .rnorm 4.802e-02 1.025e-01
## D.npnct01.log 4.255e-01 6.954e-01
## D.TfIdf.sum.stem.stop.Ratio -1.203e+01 1.816e+01
## storage.fctr16 -9.585e-01 5.717e-01
## storage.fctr32 -1.288e+00 6.034e-01
## storage.fctr64 -2.697e-01 5.973e-01
## storage.fctrUnknown -1.465e+00 7.493e-01
## D.npnct11.log 7.255e-02 4.030e-01
## D.npnct10.log -2.464e+01 1.306e+03
## D.TfIdf.sum.post.stop -1.565e+00 2.717e+00
## D.TfIdf.sum.post.stem 1.785e+00 2.826e+00
## D.sum.TfIdf NA NA
## D.npnct13.log 1.070e-01 4.207e-01
## D.npnct08.log 8.084e-01 7.614e-01
## `prdline.my.fctriPad 1` 1.425e+00 6.075e-01
## `prdline.my.fctriPad 2` 5.629e-01 6.045e-01
## `prdline.my.fctriPad 3+` 9.299e-01 5.762e-01
## prdline.my.fctriPadAir 9.884e-01 5.786e-01
## prdline.my.fctriPadmini 8.337e-01 5.692e-01
## `prdline.my.fctriPadmini 2+` 9.887e-01 6.081e-01
## color.fctrBlack 1.453e-01 2.775e-01
## color.fctrGold 2.733e-01 5.775e-01
## `color.fctrSpace Gray` -3.453e-01 3.782e-01
## color.fctrWhite -2.496e-01 2.739e-01
## D.npnct16.log 2.823e+00 1.802e+00
## D.nstopwrds.log 3.654e+00 2.192e+00
## D.npnct24.log -2.732e+00 6.281e+00
## D.npnct06.log -5.186e+00 2.224e+00
## D.npnct28.log -2.985e+00 1.722e+03
## D.nuppr.log 1.105e+00 3.441e+00
## D.nchrs.log -2.005e+00 4.149e+00
## D.npnct12.log 3.488e-01 8.201e-01
## D.nwrds.log -2.973e-01 3.411e+00
## D.npnct09.log -8.178e+00 7.891e+02
## D.ndgts.log 6.629e-01 5.764e-01
## D.terms.n.post.stop.log 3.181e+03 3.759e+03
## D.nwrds.unq.log -3.185e+03 3.759e+03
## D.terms.n.post.stem.log NA NA
## `carrier.fctrAT&T` -6.102e-01 7.651e-01
## carrier.fctrOther 1.337e+01 1.685e+03
## carrier.fctrSprint 4.735e-01 1.053e+00
## `carrier.fctrT-Mobile` -7.246e-01 1.275e+00
## carrier.fctrUnknown -4.050e-01 5.002e-01
## carrier.fctrVerizon -4.584e-02 8.272e-01
## cellular.fctr1 6.252e-01 7.047e-01
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.712e+00 1.168e+00
## D.terms.n.post.stem 2.197e+01 2.095e+01
## D.terms.n.post.stop -2.204e+01 2.090e+01
## D.npnct05.log -4.364e+00 1.823e+00
## `condition.fctrFor parts or not working` -9.262e-02 4.054e-01
## `condition.fctrManufacturer refurbished` 7.724e-01 6.645e-01
## condition.fctrNew -8.073e-02 3.367e-01
## `condition.fctrNew other (see details)` 7.400e-01 5.256e-01
## `condition.fctrSeller refurbished` -5.621e-01 4.557e-01
## idseq.my -4.934e-05 2.205e-04
## startprice.diff -1.901e-02 1.797e-03
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.878e+00 7.988e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 2.434e-01 9.002e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -1.871e-02 7.816e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.476e+00 9.489e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -9.525e-01 8.066e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 7.483e-01 8.009e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.240e-01 1.025e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` -2.815e-01 1.031e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -6.803e-01 1.038e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.521e+01 7.273e+02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -6.070e-03 7.327e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 2.538e-01 1.032e+00
## `prdline.my.fctriPadmini:.clusterid.fctr3` 7.844e-01 9.856e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -3.377e-01 1.038e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.068e+00 9.714e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 7.010e-01 1.309e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 7.209e-01 8.821e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -8.235e-01 1.097e+00
## `prdline.my.fctriPadmini:.clusterid.fctr4` -9.700e-01 9.916e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.034e+00 1.546e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -1.042e+00 1.467e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## z value Pr(>|z|)
## (Intercept) -0.826 0.4089
## biddable 14.064 <2e-16 ***
## D.ratio.nstopwrds.nwrds -1.901 0.0573 .
## D.npnct15.log 0.936 0.3495
## D.npnct03.log 0.137 0.8911
## D.terms.n.stem.stop.Ratio 0.833 0.4046
## D.ratio.sum.TfIdf.nwrds -0.505 0.6138
## .rnorm 0.468 0.6396
## D.npnct01.log 0.612 0.5406
## D.TfIdf.sum.stem.stop.Ratio -0.662 0.5077
## storage.fctr16 -1.677 0.0936 .
## storage.fctr32 -2.135 0.0328 *
## storage.fctr64 -0.451 0.6516
## storage.fctrUnknown -1.956 0.0505 .
## D.npnct11.log 0.180 0.8571
## D.npnct10.log -0.019 0.9850
## D.TfIdf.sum.post.stop -0.576 0.5644
## D.TfIdf.sum.post.stem 0.631 0.5278
## D.sum.TfIdf NA NA
## D.npnct13.log 0.254 0.7992
## D.npnct08.log 1.062 0.2884
## `prdline.my.fctriPad 1` 2.345 0.0190 *
## `prdline.my.fctriPad 2` 0.931 0.3517
## `prdline.my.fctriPad 3+` 1.614 0.1065
## prdline.my.fctriPadAir 1.708 0.0876 .
## prdline.my.fctriPadmini 1.465 0.1430
## `prdline.my.fctriPadmini 2+` 1.626 0.1040
## color.fctrBlack 0.524 0.6005
## color.fctrGold 0.473 0.6361
## `color.fctrSpace Gray` -0.913 0.3613
## color.fctrWhite -0.911 0.3621
## D.npnct16.log 1.566 0.1173
## D.nstopwrds.log 1.667 0.0954 .
## D.npnct24.log -0.435 0.6636
## D.npnct06.log -2.332 0.0197 *
## D.npnct28.log -0.002 0.9986
## D.nuppr.log 0.321 0.7481
## D.nchrs.log -0.483 0.6288
## D.npnct12.log 0.425 0.6706
## D.nwrds.log -0.087 0.9305
## D.npnct09.log -0.010 0.9917
## D.ndgts.log 1.150 0.2501
## D.terms.n.post.stop.log 0.846 0.3974
## D.nwrds.unq.log -0.847 0.3969
## D.terms.n.post.stem.log NA NA
## `carrier.fctrAT&T` -0.798 0.4252
## carrier.fctrOther 0.008 0.9937
## carrier.fctrSprint 0.449 0.6531
## `carrier.fctrT-Mobile` -0.568 0.5699
## carrier.fctrUnknown -0.810 0.4182
## carrier.fctrVerizon -0.055 0.9558
## cellular.fctr1 0.887 0.3750
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.322 0.0202 *
## D.terms.n.post.stem 1.049 0.2943
## D.terms.n.post.stop -1.055 0.2916
## D.npnct05.log -2.393 0.0167 *
## `condition.fctrFor parts or not working` -0.228 0.8193
## `condition.fctrManufacturer refurbished` 1.162 0.2451
## condition.fctrNew -0.240 0.8105
## `condition.fctrNew other (see details)` 1.408 0.1592
## `condition.fctrSeller refurbished` -1.234 0.2174
## idseq.my -0.224 0.8230
## startprice.diff -10.577 <2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.351 0.0187 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.270 0.7869
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.024 0.9809
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.555 0.1199
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.181 0.2377
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.934 0.3501
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.218 0.8271
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.273 0.7849
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.655 0.5122
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.021 0.9833
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.008 0.9934
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.246 0.8058
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.796 0.4261
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.325 0.7449
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.129 0.0332 *
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.536 0.5923
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.817 0.4138
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.751 0.4528
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.978 0.3280
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.963 0.0496 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.710 0.4776
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 713.28 on 892 degrees of freedom
## AIC: 877.28
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7362270
## 3 0.2 0.7809879
## 4 0.3 0.8004053
## 5 0.4 0.8217391
## 6 0.5 0.8213879
## 7 0.6 0.8162291
## 8 0.7 0.7975000
## 9 0.8 0.7463087
## 10 0.9 0.6142209
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 432 92
## 2 Y 72 378
## Prediction
## Reference N Y
## N 432 92
## Y 72 378
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.316222e-01 6.623489e-01 8.066148e-01 8.546053e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.125901e-83 1.379016e-01
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7172285
## 3 0.2 0.7338877
## 4 0.3 0.7488584
## 5 0.4 0.7629362
## 6 0.5 0.7732997
## 7 0.6 0.7769029
## 8 0.7 0.7738420
## 9 0.8 0.7314949
## 10 0.9 0.5963756
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 419 56
## 2 Y 114 296
## Prediction
## Reference N Y
## N 419 56
## Y 114 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.079096e-01 6.099603e-01 7.803820e-01 8.333692e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.732450e-64 1.232826e-05
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.571 0.25
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9130577 0.4 0.8217391 0.7905476
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8066148 0.8546053 0.5768469 0.8492837
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7769029 0.8079096
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.780382 0.8333692 0.6099603 877.2778
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01356908 0.02894366
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 129.497 134.97 5.473
## 3 fit.models_1_bayesglm 3 0 134.970 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.93999 -0.59559 -0.09853 0.49955 2.58702
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 5.017e+00 6.711e+00
## biddable 3.531e+00 2.439e-01
## D.ratio.nstopwrds.nwrds -2.017e+00 2.438e+00
## D.npnct15.log 1.113e+00 8.606e-01
## D.npnct03.log 9.030e-03 1.806e+00
## D.terms.n.stem.stop.Ratio -3.431e+00 5.543e+00
## D.ratio.sum.TfIdf.nwrds -3.469e-01 2.983e-01
## .rnorm 4.706e-02 9.810e-02
## D.npnct01.log 3.597e-01 5.936e-01
## D.TfIdf.sum.stem.stop.Ratio -6.399e-01 3.473e+00
## storage.fctr16 -7.576e-01 4.864e-01
## storage.fctr32 -1.060e+00 5.163e-01
## storage.fctr64 -9.463e-02 5.128e-01
## storage.fctrUnknown -1.143e+00 6.441e-01
## D.npnct11.log 3.760e-02 3.578e-01
## D.npnct10.log -7.700e+00 7.489e+00
## D.TfIdf.sum.post.stop 2.641e-02 2.897e-01
## D.TfIdf.sum.post.stem 6.814e-02 3.057e-01
## D.sum.TfIdf 6.814e-02 3.057e-01
## D.npnct13.log 2.215e-02 3.520e-01
## D.npnct08.log 6.169e-01 7.151e-01
## `prdline.my.fctriPad 1` 1.004e+00 5.071e-01
## `prdline.my.fctriPad 2` 3.190e-01 5.101e-01
## `prdline.my.fctriPad 3+` 6.673e-01 4.809e-01
## prdline.my.fctriPadAir 6.916e-01 4.801e-01
## prdline.my.fctriPadmini 5.393e-01 4.733e-01
## `prdline.my.fctriPadmini 2+` 6.522e-01 5.122e-01
## color.fctrBlack 1.399e-01 2.622e-01
## color.fctrGold 2.539e-01 5.306e-01
## `color.fctrSpace Gray` -3.804e-01 3.543e-01
## color.fctrWhite -2.719e-01 2.582e-01
## D.npnct16.log 2.433e+00 1.578e+00
## D.nstopwrds.log 5.842e-01 6.570e-01
## D.npnct24.log 3.539e-01 2.371e+00
## D.npnct06.log -4.576e+00 1.944e+00
## D.npnct28.log -5.536e-02 2.192e+00
## D.nuppr.log 3.458e-02 4.811e-01
## D.nchrs.log -3.289e-02 4.804e-01
## D.npnct12.log 2.079e-01 7.355e-01
## D.nwrds.log -3.725e-03 7.730e-01
## D.npnct09.log -1.469e+00 5.409e+00
## D.ndgts.log 7.901e-01 4.370e-01
## D.terms.n.post.stop.log -2.057e-01 1.029e+00
## D.nwrds.unq.log -2.073e-01 1.033e+00
## D.terms.n.post.stem.log -2.073e-01 1.033e+00
## `carrier.fctrAT&T` -3.393e-01 7.754e-01
## carrier.fctrOther 5.292e-01 1.875e+00
## carrier.fctrSprint 6.592e-01 9.635e-01
## `carrier.fctrT-Mobile` -4.695e-01 1.063e+00
## carrier.fctrUnknown -2.394e-01 7.932e-01
## carrier.fctrVerizon 1.354e-01 8.093e-01
## cellular.fctr1 3.757e-01 7.436e-01
## cellular.fctrUnknown -2.856e-01 8.741e-01
## D.npnct14.log -2.319e+00 1.011e+00
## D.terms.n.post.stem -1.023e-01 2.087e-01
## D.terms.n.post.stop -1.360e-01 2.082e-01
## D.npnct05.log -3.766e+00 1.533e+00
## `condition.fctrFor parts or not working` -6.015e-02 3.805e-01
## `condition.fctrManufacturer refurbished` 7.234e-01 6.199e-01
## condition.fctrNew -9.021e-02 3.189e-01
## `condition.fctrNew other (see details)` 6.658e-01 4.839e-01
## `condition.fctrSeller refurbished` -4.934e-01 4.176e-01
## idseq.my -8.742e-05 2.107e-04
## startprice.diff -1.764e-02 1.644e-03
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.545e+00 6.737e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 3.164e-01 7.453e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.204e-01 6.629e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.110e+00 7.639e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -8.374e-01 6.801e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 6.803e-01 7.050e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.579e-01 8.466e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -4.908e-01 8.139e-01
## `prdline.my.fctriPad 1:.clusterid.fctr3` -3.187e-01 8.499e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.886e+00 1.559e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -9.173e-02 6.338e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 1.792e-01 8.554e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 7.424e-01 8.071e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -3.665e-01 8.553e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000e+00 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.872e+00 8.345e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 4.642e-01 1.030e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 6.549e-01 7.348e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -4.193e-01 7.946e-01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -6.988e-01 8.143e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000e+00 2.500e+00
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.569e+00 1.199e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPadmini:.clusterid.fctr5` -7.105e-01 1.170e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000e+00 2.500e+00
## z value Pr(>|z|)
## (Intercept) 0.748 0.4548
## biddable 14.474 <2e-16 ***
## D.ratio.nstopwrds.nwrds -0.827 0.4082
## D.npnct15.log 1.293 0.1960
## D.npnct03.log 0.005 0.9960
## D.terms.n.stem.stop.Ratio -0.619 0.5360
## D.ratio.sum.TfIdf.nwrds -1.163 0.2449
## .rnorm 0.480 0.6314
## D.npnct01.log 0.606 0.5445
## D.TfIdf.sum.stem.stop.Ratio -0.184 0.8538
## storage.fctr16 -1.557 0.1194
## storage.fctr32 -2.053 0.0401 *
## storage.fctr64 -0.185 0.8536
## storage.fctrUnknown -1.775 0.0759 .
## D.npnct11.log 0.105 0.9163
## D.npnct10.log -1.028 0.3038
## D.TfIdf.sum.post.stop 0.091 0.9274
## D.TfIdf.sum.post.stem 0.223 0.8236
## D.sum.TfIdf 0.223 0.8236
## D.npnct13.log 0.063 0.9498
## D.npnct08.log 0.863 0.3883
## `prdline.my.fctriPad 1` 1.980 0.0477 *
## `prdline.my.fctriPad 2` 0.625 0.5317
## `prdline.my.fctriPad 3+` 1.388 0.1653
## prdline.my.fctriPadAir 1.440 0.1498
## prdline.my.fctriPadmini 1.140 0.2545
## `prdline.my.fctriPadmini 2+` 1.273 0.2029
## color.fctrBlack 0.534 0.5936
## color.fctrGold 0.478 0.6323
## `color.fctrSpace Gray` -1.074 0.2829
## color.fctrWhite -1.053 0.2922
## D.npnct16.log 1.542 0.1232
## D.nstopwrds.log 0.889 0.3739
## D.npnct24.log 0.149 0.8813
## D.npnct06.log -2.354 0.0186 *
## D.npnct28.log -0.025 0.9798
## D.nuppr.log 0.072 0.9427
## D.nchrs.log -0.068 0.9454
## D.npnct12.log 0.283 0.7774
## D.nwrds.log -0.005 0.9962
## D.npnct09.log -0.272 0.7860
## D.ndgts.log 1.808 0.0706 .
## D.terms.n.post.stop.log -0.200 0.8416
## D.nwrds.unq.log -0.201 0.8409
## D.terms.n.post.stem.log -0.201 0.8409
## `carrier.fctrAT&T` -0.438 0.6617
## carrier.fctrOther 0.282 0.7778
## carrier.fctrSprint 0.684 0.4939
## `carrier.fctrT-Mobile` -0.442 0.6587
## carrier.fctrUnknown -0.302 0.7628
## carrier.fctrVerizon 0.167 0.8671
## cellular.fctr1 0.505 0.6134
## cellular.fctrUnknown -0.327 0.7439
## D.npnct14.log -2.294 0.0218 *
## D.terms.n.post.stem -0.490 0.6241
## D.terms.n.post.stop -0.653 0.5137
## D.npnct05.log -2.457 0.0140 *
## `condition.fctrFor parts or not working` -0.158 0.8744
## `condition.fctrManufacturer refurbished` 1.167 0.2432
## condition.fctrNew -0.283 0.7773
## `condition.fctrNew other (see details)` 1.376 0.1689
## `condition.fctrSeller refurbished` -1.181 0.2374
## idseq.my -0.415 0.6783
## startprice.diff -10.731 <2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.294 0.0218 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.425 0.6712
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.182 0.8558
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.453 0.1462
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.231 0.2182
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.965 0.3345
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.423 0.6725
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.603 0.5465
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.375 0.7077
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.210 0.2264
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.145 0.8849
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.210 0.8340
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.920 0.3576
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.429 0.6683
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.243 0.0249 *
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.451 0.6522
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.891 0.3728
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.528 0.5978
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.858 0.3908
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.142 0.0322 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.607 0.5438
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 722.59 on 882 degrees of freedom
## AIC: 906.59
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7295285
## 3 0.2 0.7746090
## 4 0.3 0.7979798
## 5 0.4 0.8129730
## 6 0.5 0.8164196
## 7 0.6 0.8238095
## 8 0.7 0.7979925
## 9 0.8 0.7563249
## 10 0.9 0.5736677
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 480
## 2 Y 104
## sold.fctr.predict.All.X.bayesglm.Y
## 1 44
## 2 346
## Prediction
## Reference N Y
## N 480 44
## Y 104 346
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.480493e-01 6.914291e-01 8.239439e-01 8.700277e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 7.437206e-94 1.236046e-06
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7157314
## 3 0.2 0.7414330
## 4 0.3 0.7542857
## 5 0.4 0.7699758
## 6 0.5 0.7741117
## 7 0.6 0.7830688
## 8 0.7 0.7626886
## 9 0.8 0.7288630
## 10 0.9 0.5617597
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 425
## 2 Y 114
## sold.fctr.predict.All.X.bayesglm.Y
## 1 50
## 2 296
## Prediction
## Reference N Y
## N 425 50
## Y 114 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.146893e-01 6.233456e-01 7.874927e-01 8.397715e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.806781e-67 8.677732e-07
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.587 0.365
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9103774 0.6 0.8238095 0.794666
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8239439 0.8700277 0.5859868 0.8576175
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7830688 0.8146893
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7874927 0.8397715 0.6233456 906.5894
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004364881 0.0108705
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 134.970 140.831 5.861
## 4 fit.models_1_glmnet 4 0 140.832 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 91 -none- numeric
## beta 8281 dgCMatrix S4
## df 91 -none- numeric
## dim 2 -none- numeric
## lambda 91 -none- numeric
## dev.ratio 91 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 91 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -8.321452e-01
## biddable
## 2.005705e+00
## carrier.fctrUnknown
## -6.162401e-02
## cellular.fctrUnknown
## -1.481842e-01
## D.npnct14.log
## -1.836343e-01
## D.npnct05.log
## -4.512526e-01
## idseq.my
## -2.336508e-05
## startprice.diff
## -6.938104e-03
## prdline.my.fctriPad 2:.clusterid.fctr5
## 2.993735e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.086946e+01
## biddable
## 3.672327e+00
## D.ratio.nstopwrds.nwrds
## -6.385701e+00
## D.npnct15.log
## 1.013264e+00
## D.npnct03.log
## -8.840403e-02
## D.terms.n.stem.stop.Ratio
## -4.763888e+00
## D.ratio.sum.TfIdf.nwrds
## -3.753543e-01
## .rnorm
## 5.116733e-02
## D.npnct01.log
## 3.536558e-01
## D.TfIdf.sum.stem.stop.Ratio
## -9.004306e-01
## storage.fctr16
## -9.708011e-01
## storage.fctr32
## -1.310240e+00
## storage.fctr64
## -2.903630e-01
## storage.fctrUnknown
## -1.416895e+00
## D.npnct11.log
## 2.542187e-02
## D.npnct10.log
## -1.057664e+01
## D.TfIdf.sum.post.stem
## 8.295661e-02
## D.sum.TfIdf
## 7.594618e-02
## D.npnct13.log
## -6.481685e-03
## D.npnct08.log
## 7.075630e-01
## prdline.my.fctriPad 1
## 1.338232e+00
## prdline.my.fctriPad 2
## 5.277989e-01
## prdline.my.fctriPad 3+
## 9.149489e-01
## prdline.my.fctriPadAir
## 9.483600e-01
## prdline.my.fctriPadmini
## 7.958867e-01
## prdline.my.fctriPadmini 2+
## 9.295808e-01
## color.fctrBlack
## 1.514419e-01
## color.fctrGold
## 2.389034e-01
## color.fctrSpace Gray
## -3.992837e-01
## color.fctrWhite
## -2.905883e-01
## D.npnct16.log
## 2.754961e+00
## D.nstopwrds.log
## 1.647224e+00
## D.npnct24.log
## -9.430424e-01
## D.npnct06.log
## -5.104860e+00
## D.npnct28.log
## -6.207069e-01
## D.nuppr.log
## -1.351991e-04
## D.nchrs.log
## -2.562800e-01
## D.npnct12.log
## 2.742386e-01
## D.nwrds.log
## -9.089358e-03
## D.npnct09.log
## -1.637804e+00
## D.ndgts.log
## 8.262264e-01
## D.terms.n.post.stop.log
## -8.293690e-01
## D.nwrds.unq.log
## -5.299870e-01
## D.terms.n.post.stem.log
## -2.821183e-01
## carrier.fctrAT&T
## -1.320782e-01
## carrier.fctrOther
## 4.437442e+00
## carrier.fctrSprint
## 9.716091e-01
## carrier.fctrT-Mobile
## -3.234630e-01
## carrier.fctrVerizon
## 3.735786e-01
## cellular.fctr1
## 1.544687e-01
## cellular.fctrUnknown
## -4.092341e-01
## D.npnct14.log
## -2.543977e+00
## D.terms.n.post.stop
## -2.377653e-01
## D.npnct05.log
## -4.528947e+00
## condition.fctrFor parts or not working
## -1.156302e-01
## condition.fctrManufacturer refurbished
## 8.330234e-01
## condition.fctrNew
## -7.711747e-02
## condition.fctrNew other (see details)
## 7.537808e-01
## condition.fctrSeller refurbished
## -5.236321e-01
## idseq.my
## -6.290456e-05
## startprice.diff
## -1.845350e-02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 1.754541e+00
## prdline.my.fctriPad 1:.clusterid.fctr2
## 1.792593e-01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 7.962901e-02
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.453073e+00
## prdline.my.fctriPadAir:.clusterid.fctr2
## -1.001920e+00
## prdline.my.fctriPadmini:.clusterid.fctr2
## 7.023608e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.486295e-01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -3.826176e-01
## prdline.my.fctriPad 1:.clusterid.fctr3
## -5.469441e-01
## prdline.my.fctriPad 2:.clusterid.fctr3
## -5.841502e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -1.025428e-01
## prdline.my.fctriPadAir:.clusterid.fctr3
## 1.602461e-01
## prdline.my.fctriPadmini:.clusterid.fctr3
## 7.811551e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -5.094387e-01
## prdline.my.fctriPad 1:.clusterid.fctr4
## -2.200274e+00
## prdline.my.fctriPad 2:.clusterid.fctr4
## 5.902645e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 7.284231e-01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -7.137771e-01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -9.480595e-01
## prdline.my.fctriPad 2:.clusterid.fctr5
## 3.140443e+00
## prdline.my.fctriPadmini:.clusterid.fctr5
## -1.133474e+00
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.63202247
## 2 0.1 0.64841499
## 3 0.2 0.68857590
## 4 0.3 0.74787535
## 5 0.4 0.75496689
## 6 0.5 0.77152698
## 7 0.6 0.80484848
## 8 0.7 0.77631579
## 9 0.8 0.23137255
## 10 0.9 0.00443459
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 481
## 2 Y 118
## sold.fctr.predict.All.X.glmnet.Y
## 1 43
## 2 332
## Prediction
## Reference N Y
## N 481 43
## Y 118 332
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.347023e-01 6.635254e-01 8.098576e-01 8.575034e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.696932e-85 5.476134e-09
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.633204633
## 2 0.1 0.648648649
## 3 0.2 0.694827586
## 4 0.3 0.734358974
## 5 0.4 0.754017305
## 6 0.5 0.778350515
## 7 0.6 0.796221323
## 8 0.7 0.776671408
## 9 0.8 0.248945148
## 10 0.9 0.009708738
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 439
## 2 Y 115
## sold.fctr.predict.All.X.glmnet.Y
## 1 36
## 2 295
## Prediction
## Reference N Y
## N 439 36
## Y 115 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.293785e-01 6.523227e-01 8.029439e-01 8.535980e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 2.796139e-75 2.187709e-10
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 5.217 0.961
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8773155 0.6 0.8048485 0.7977588
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8098576 0.8575034 0.5910839 0.8631682
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7962213 0.8293785
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8029439 0.853598 0.6523227
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01547524 0.03049648
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_glmnet 4 0 140.832 150.108 9.276
## 5 fit.models_1_rpart 5 0 150.108 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00833 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.148888889 1 0.4888889
## 3 0.008333333 2 0.3400000
##
## Variable importance
## biddable
## 49
## startprice.diff
## 30
## idseq.my
## 10
## condition.fctrFor parts or not working
## 3
## prdline.my.fctriPad 1
## 2
## prdline.my.fctriPad 2
## 2
## D.ratio.sum.TfIdf.nwrds
## 2
## color.fctrGold
## 1
## D.ratio.nstopwrds.nwrds
## 1
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.149900, (0 missing)
## startprice.diff < 44.18097 to the right, improve= 78.497850, (0 missing)
## idseq.my < 905.5 to the right, improve= 38.912990, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 10.867390, (0 missing)
## D.nwrds.unq.log < 2.138333 to the right, improve= 9.295085, (0 missing)
## Surrogate splits:
## idseq.my < 869 to the right, agree=0.636, adj=0.211, (0 split)
## condition.fctrFor parts or not working < 0.5 to the left, agree=0.563, adj=0.053, (0 split)
## prdline.my.fctriPad 1 < 0.5 to the left, agree=0.559, adj=0.044, (0 split)
## prdline.my.fctriPad 2 < 0.5 to the left, agree=0.553, adj=0.033, (0 split)
## D.ratio.sum.TfIdf.nwrds < 0.9315387 to the left, agree=0.552, adj=0.031, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.1488889
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (89 obs) right son=7 (361 obs)
## Primary splits:
## startprice.diff < 84.72197 to the right, improve=88.614450, (0 missing)
## idseq.my < 670.5 to the right, improve=15.110620, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 3.467222, (0 missing)
## carrier.fctrUnknown < 0.5 to the right, improve= 3.372762, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve= 2.987781, (0 missing)
## Surrogate splits:
## color.fctrGold < 0.5 to the right, agree=0.809, adj=0.034, (0 split)
## D.ratio.nstopwrds.nwrds < 0.1380952 to the left, agree=0.807, adj=0.022, (0 split)
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 < 0.5 to the right, agree=0.804, adj=0.011, (0 split)
##
## Node number 6: 89 observations
## predicted class=N expected loss=0.1235955 P(node) =0.09137577
## class counts: 78 11
## probabilities: 0.876 0.124
##
## Node number 7: 361 observations
## predicted class=Y expected loss=0.08864266 P(node) =0.3706366
## class counts: 32 329
## probabilities: 0.089 0.911
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=84.72197 89 11 N (0.87640449 0.12359551) *
## 7) startprice.diff< 84.72197 361 32 Y (0.08864266 0.91135734) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6576779
## 4 0.3 0.8113440
## 5 0.4 0.8113440
## 6 0.5 0.8113440
## 7 0.6 0.8113440
## 8 0.7 0.8113440
## 9 0.8 0.8113440
## 10 0.9 0.8113440
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 492
## 2 Y 121
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 32
## 2 329
## Prediction
## Reference N Y
## N 492 32
## Y 121 329
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.429158e-01 6.795322e-01 8.185193e-01 8.652175e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.699210e-90 1.124184e-12
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6515397
## 4 0.3 0.7934783
## 5 0.4 0.7934783
## 6 0.5 0.7934783
## 7 0.6 0.7934783
## 8 0.7 0.7934783
## 9 0.8 0.7934783
## 10 0.9 0.7934783
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 441
## 2 Y 118
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 34
## 2 292
## Prediction
## Reference N Y
## N 441 34
## Y 118 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.282486e-01 6.497240e-01 8.017531e-01 8.525367e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.168374e-74 1.671294e-11
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.673 0.063
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8435581 0.9 0.811344 0.8347135
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8185193 0.8652175 0.6642538 0.8273068
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.7934783 0.8282486
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8017531 0.8525367 0.649724
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0167898 0.03471308
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rpart 5 0 150.108 155.583 5.475
## 6 fit.models_1_rf 6 0 155.584 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 46 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 90 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 90 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.8571429
## 3 0.2 0.9513742
## 4 0.3 0.9814613
## 5 0.4 0.9988901
## 6 0.5 1.0000000
## 7 0.6 1.0000000
## 8 0.7 0.9595376
## 9 0.8 0.8833747
## 10 0.9 0.8157895
## 11 1.0 0.1670061
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 524
## 2 Y NA
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 450
## Prediction
## Reference N Y
## N 524 0
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.962198e-01 1.000000e+00 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.919016e-263 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.63320463
## 2 0.1 0.71002710
## 3 0.2 0.76530612
## 4 0.3 0.78923767
## 5 0.4 0.78817734
## 6 0.5 0.80733945
## 7 0.6 0.80868385
## 8 0.7 0.80166436
## 9 0.8 0.77187948
## 10 0.9 0.69266771
## 11 1.0 0.09280742## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 446
## 2 Y 112
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 29
## 2 298
## Prediction
## Reference N Y
## N 446 29
## Y 112 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.406780e-01 6.751279e-01 8.148743e-01 8.641887e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.104950e-81 4.997557e-12
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 15.404 4.801
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.6 1 0.8418803
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9962198 1 0.6791071 0.8876893
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.8086839 0.840678
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8148743 0.8641887 0.6751279
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01707264 0.03596915
## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rf 6 0 155.584 174.447 18.864
## 7 fit.models_1_glm 7 0 174.448 NA NA
## [1] "fitting model: All.Interact.X.glm"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2109 -0.5090 -0.0376 0.4264 2.5624
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -2.386e+03 3.737e+03
## D.ratio.nstopwrds.nwrds -1.459e+01 8.478e+00
## D.npnct15.log 5.362e-01 9.757e-01
## D.npnct03.log -3.198e-01 2.299e+00
## D.terms.n.stem.stop.Ratio 2.406e+03 3.736e+03
## D.ratio.sum.TfIdf.nwrds -3.172e-01 7.169e-01
## .rnorm 1.717e-02 1.094e-01
## D.npnct01.log 2.924e-01 7.696e-01
## D.TfIdf.sum.stem.stop.Ratio -6.661e+00 2.076e+01
## storage.fctr16 -8.690e-01 6.335e-01
## storage.fctr32 -1.333e+00 6.749e-01
## storage.fctr64 7.130e-02 6.602e-01
## storage.fctrUnknown -1.509e+00 8.082e-01
## D.npnct11.log 3.976e-01 4.343e-01
## D.npnct10.log -2.675e+01 2.110e+03
## D.TfIdf.sum.post.stop -7.361e-01 3.092e+00
## D.TfIdf.sum.post.stem 9.763e-01 3.206e+00
## D.sum.TfIdf NA NA
## D.npnct13.log 3.999e-01 4.653e-01
## D.npnct08.log 7.605e-01 8.308e-01
## `prdline.my.fctriPad 1` 1.545e-01 1.293e+00
## `prdline.my.fctriPad 2` 1.600e-01 1.337e+00
## `prdline.my.fctriPad 3+` 1.817e+00 1.185e+00
## prdline.my.fctriPadAir 1.179e+00 1.177e+00
## prdline.my.fctriPadmini 1.292e+00 1.181e+00
## `prdline.my.fctriPadmini 2+` 2.729e-01 1.336e+00
## color.fctrBlack 2.158e-01 3.054e-01
## color.fctrGold 1.702e-02 6.170e-01
## `color.fctrSpace Gray` -4.507e-01 3.895e-01
## color.fctrWhite -2.766e-01 2.916e-01
## D.npnct16.log 4.157e+00 2.106e+00
## D.nstopwrds.log 4.081e+00 2.471e+00
## D.npnct24.log 6.497e-01 7.111e+00
## D.npnct06.log -7.284e+00 2.562e+00
## D.npnct28.log -3.428e+00 2.679e+03
## D.nuppr.log 2.139e+00 3.951e+00
## D.nchrs.log -3.853e+00 4.735e+00
## D.npnct12.log 1.595e-02 8.749e-01
## D.nwrds.log -2.934e-01 3.633e+00
## D.npnct09.log -8.670e+00 1.027e+03
## D.ndgts.log 1.286e+00 6.556e-01
## D.terms.n.post.stop.log 2.716e+03 4.161e+03
## D.nwrds.unq.log -2.720e+03 4.161e+03
## D.terms.n.post.stem.log NA NA
## `carrier.fctrAT&T` -1.142e+00 8.462e-01
## carrier.fctrOther 1.319e+01 2.711e+03
## carrier.fctrSprint -1.199e-01 1.123e+00
## `carrier.fctrT-Mobile` -1.839e+00 1.356e+00
## carrier.fctrUnknown -5.792e-01 5.306e-01
## carrier.fctrVerizon -4.572e-01 9.079e-01
## cellular.fctr1 1.299e+00 7.822e-01
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.590e+00 1.255e+00
## D.terms.n.post.stem 1.863e+01 2.292e+01
## D.terms.n.post.stop -1.862e+01 2.286e+01
## D.npnct05.log -5.154e+00 1.951e+00
## `condition.fctrFor parts or not working` 3.579e-02 4.456e-01
## `condition.fctrManufacturer refurbished` 5.735e-01 7.083e-01
## condition.fctrNew 9.478e-03 3.541e-01
## `condition.fctrNew other (see details)` 8.008e-01 5.590e-01
## `condition.fctrSeller refurbished` -5.208e-01 5.076e-01
## startprice.diff -9.449e-03 3.045e-03
## biddable 3.664e+00 7.988e-01
## idseq.my 2.886e-04 6.684e-04
## `prdline.my.fctriPad 1:startprice.diff` -6.907e-02 1.928e-02
## `prdline.my.fctriPad 2:startprice.diff` -1.071e-02 7.195e-03
## `prdline.my.fctriPad 3+:startprice.diff` -2.432e-02 6.670e-03
## `prdline.my.fctriPadAir:startprice.diff` -7.676e-03 4.360e-03
## `prdline.my.fctriPadmini:startprice.diff` -1.663e-02 6.154e-03
## `prdline.my.fctriPadmini 2+:startprice.diff` -1.459e-02 6.636e-03
## `prdline.my.fctriPad 1:biddable` 2.168e+00 1.286e+00
## `prdline.my.fctriPad 2:biddable` 1.272e+00 1.165e+00
## `prdline.my.fctriPad 3+:biddable` -2.110e-01 9.892e-01
## `prdline.my.fctriPadAir:biddable` 5.935e-01 9.813e-01
## `prdline.my.fctriPadmini:biddable` -5.966e-01 9.521e-01
## `prdline.my.fctriPadmini 2+:biddable` 1.223e-01 1.152e+00
## `prdline.my.fctriPad 1:idseq.my` 7.891e-04 8.842e-04
## `prdline.my.fctriPad 2:idseq.my` -8.965e-04 9.411e-04
## `prdline.my.fctriPad 3+:idseq.my` -7.111e-04 7.793e-04
## `prdline.my.fctriPadAir:idseq.my` -8.596e-04 8.126e-04
## `prdline.my.fctriPadmini:idseq.my` -4.353e-04 7.720e-04
## `prdline.my.fctriPadmini 2+:idseq.my` 4.430e-04 9.053e-04
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.765e+00 8.568e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -1.502e-01 9.820e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.732e-01 9.036e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.545e+00 9.816e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.030e+00 8.597e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 6.071e-01 8.246e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 5.482e-01 1.112e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` -7.968e-01 1.091e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -9.577e-01 1.433e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.554e+01 1.154e+03
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.053e-01 7.986e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 2.301e-01 1.082e+00
## `prdline.my.fctriPadmini:.clusterid.fctr3` 3.990e-01 9.753e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -8.010e-01 1.098e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.205e+00 1.412e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.399e+00 1.465e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 7.748e-01 1.021e+00
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.236e+00 1.161e+00
## `prdline.my.fctriPadmini:.clusterid.fctr4` -6.811e-01 9.914e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.776e+00 1.676e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -5.360e-01 1.505e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## z value Pr(>|z|)
## (Intercept) -0.638 0.523238
## D.ratio.nstopwrds.nwrds -1.721 0.085326 .
## D.npnct15.log 0.550 0.582637
## D.npnct03.log -0.139 0.889355
## D.terms.n.stem.stop.Ratio 0.644 0.519660
## D.ratio.sum.TfIdf.nwrds -0.442 0.658176
## .rnorm 0.157 0.875245
## D.npnct01.log 0.380 0.704025
## D.TfIdf.sum.stem.stop.Ratio -0.321 0.748287
## storage.fctr16 -1.372 0.170146
## storage.fctr32 -1.975 0.048212 *
## storage.fctr64 0.108 0.913992
## storage.fctrUnknown -1.868 0.061806 .
## D.npnct11.log 0.916 0.359873
## D.npnct10.log -0.013 0.989888
## D.TfIdf.sum.post.stop -0.238 0.811800
## D.TfIdf.sum.post.stem 0.304 0.760755
## D.sum.TfIdf NA NA
## D.npnct13.log 0.859 0.390083
## D.npnct08.log 0.915 0.359977
## `prdline.my.fctriPad 1` 0.120 0.904844
## `prdline.my.fctriPad 2` 0.120 0.904698
## `prdline.my.fctriPad 3+` 1.534 0.124989
## prdline.my.fctriPadAir 1.002 0.316454
## prdline.my.fctriPadmini 1.095 0.273721
## `prdline.my.fctriPadmini 2+` 0.204 0.838152
## color.fctrBlack 0.707 0.479669
## color.fctrGold 0.028 0.977996
## `color.fctrSpace Gray` -1.157 0.247189
## color.fctrWhite -0.949 0.342860
## D.npnct16.log 1.974 0.048354 *
## D.nstopwrds.log 1.651 0.098648 .
## D.npnct24.log 0.091 0.927204
## D.npnct06.log -2.842 0.004477 **
## D.npnct28.log -0.001 0.998979
## D.nuppr.log 0.541 0.588228
## D.nchrs.log -0.814 0.415730
## D.npnct12.log 0.018 0.985451
## D.nwrds.log -0.081 0.935622
## D.npnct09.log -0.008 0.993268
## D.ndgts.log 1.961 0.049849 *
## D.terms.n.post.stop.log 0.653 0.513879
## D.nwrds.unq.log -0.654 0.513272
## D.terms.n.post.stem.log NA NA
## `carrier.fctrAT&T` -1.349 0.177334
## carrier.fctrOther 0.005 0.996119
## carrier.fctrSprint -0.107 0.914964
## `carrier.fctrT-Mobile` -1.356 0.175022
## carrier.fctrUnknown -1.092 0.274998
## carrier.fctrVerizon -0.504 0.614534
## cellular.fctr1 1.661 0.096678 .
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.064 0.039029 *
## D.terms.n.post.stem 0.813 0.416186
## D.terms.n.post.stop -0.815 0.415264
## D.npnct05.log -2.641 0.008263 **
## `condition.fctrFor parts or not working` 0.080 0.935979
## `condition.fctrManufacturer refurbished` 0.810 0.418133
## condition.fctrNew 0.027 0.978648
## `condition.fctrNew other (see details)` 1.433 0.151986
## `condition.fctrSeller refurbished` -1.026 0.304868
## startprice.diff -3.103 0.001915 **
## biddable 4.586 4.51e-06 ***
## idseq.my 0.432 0.665914
## `prdline.my.fctriPad 1:startprice.diff` -3.583 0.000339 ***
## `prdline.my.fctriPad 2:startprice.diff` -1.488 0.136741
## `prdline.my.fctriPad 3+:startprice.diff` -3.647 0.000266 ***
## `prdline.my.fctriPadAir:startprice.diff` -1.760 0.078345 .
## `prdline.my.fctriPadmini:startprice.diff` -2.702 0.006887 **
## `prdline.my.fctriPadmini 2+:startprice.diff` -2.198 0.027943 *
## `prdline.my.fctriPad 1:biddable` 1.686 0.091781 .
## `prdline.my.fctriPad 2:biddable` 1.091 0.275110
## `prdline.my.fctriPad 3+:biddable` -0.213 0.831112
## `prdline.my.fctriPadAir:biddable` 0.605 0.545344
## `prdline.my.fctriPadmini:biddable` -0.627 0.530861
## `prdline.my.fctriPadmini 2+:biddable` 0.106 0.915462
## `prdline.my.fctriPad 1:idseq.my` 0.892 0.372156
## `prdline.my.fctriPad 2:idseq.my` -0.953 0.340784
## `prdline.my.fctriPad 3+:idseq.my` -0.912 0.361548
## `prdline.my.fctriPadAir:idseq.my` -1.058 0.290106
## `prdline.my.fctriPadmini:idseq.my` -0.564 0.572845
## `prdline.my.fctriPadmini 2+:idseq.my` 0.489 0.624638
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.060 0.039374 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.153 0.878405
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.192 0.847964
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.574 0.115406
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.198 0.230997
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.736 0.461598
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.493 0.622089
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.730 0.465393
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.668 0.503902
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.013 0.989258
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.132 0.895134
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.213 0.831578
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.409 0.682461
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.729 0.465721
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.562 0.118230
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.954 0.339887
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.759 0.448125
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.065 0.286754
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.687 0.492117
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.253 0.024247 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.356 0.721696
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 653.29 on 874 degrees of freedom
## AIC: 853.29
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7610009
## 3 0.2 0.8041825
## 4 0.3 0.8212462
## 5 0.4 0.8396125
## 6 0.5 0.8331442
## 7 0.6 0.8335301
## 8 0.7 0.8099379
## 9 0.8 0.7700394
## 10 0.9 0.6460177
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.glm.N
## 1 N 435
## 2 Y 60
## sold.fctr.predict.All.Interact.X.glm.Y
## 1 89
## 2 390
## Prediction
## Reference N Y
## N 435 89
## Y 60 390
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.470226e-01 6.936628e-01 8.228583e-01 8.690663e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.548086e-93 2.179915e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7247796
## 3 0.2 0.7459459
## 4 0.3 0.7560694
## 5 0.4 0.7733333
## 6 0.5 0.7878788
## 7 0.6 0.7894737
## 8 0.7 0.7711172
## 9 0.8 0.7341040
## 10 0.9 0.6368000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.glm.N
## 1 N 425
## 2 Y 110
## sold.fctr.predict.All.Interact.X.glm.Y
## 1 50
## 2 300
## Prediction
## Reference N Y
## N 425 50
## Y 110 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.192090e-01 6.327801e-01 7.922402e-01 8.440325e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 8.195907e-70 3.095797e-06
## model_id model_method
## 1 All.Interact.X.glm glm
## feats
## 1 D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.836 0.373
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9285072 0.4 0.8396125 0.7844033
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8228583 0.8690663 0.5662735 0.855923
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7894737 0.819209
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7922402 0.8440325 0.6327801 853.2895
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03020935 0.06241819
## label step_major step_minor bgn end elapsed
## 7 fit.models_1_glm 7 0 174.448 180.31 5.862
## 8 fit.models_1_bayesglm 8 0 180.311 NA NA
## [1] "fitting model: All.Interact.X.bayesglm"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.05377 -0.54115 -0.08022 0.46910 2.38376
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 1.856e+00 7.035e+00
## D.ratio.nstopwrds.nwrds -1.672e+00 2.446e+00
## D.npnct15.log 6.491e-01 9.140e-01
## D.npnct03.log -7.968e-01 1.891e+00
## D.terms.n.stem.stop.Ratio -7.348e-01 5.847e+00
## D.ratio.sum.TfIdf.nwrds -3.077e-01 3.047e-01
## .rnorm 2.644e-02 1.035e-01
## D.npnct01.log 2.114e-01 6.478e-01
## D.TfIdf.sum.stem.stop.Ratio -4.473e-01 3.662e+00
## storage.fctr16 -6.947e-01 5.185e-01
## storage.fctr32 -1.092e+00 5.559e-01
## storage.fctr64 1.482e-01 5.468e-01
## storage.fctrUnknown -1.161e+00 6.701e-01
## D.npnct11.log 1.990e-01 3.768e-01
## D.npnct10.log -8.147e+00 7.369e+00
## D.TfIdf.sum.post.stop 4.349e-02 2.914e-01
## D.TfIdf.sum.post.stem 6.711e-02 3.064e-01
## D.sum.TfIdf 6.711e-02 3.064e-01
## D.npnct13.log 1.670e-01 3.724e-01
## D.npnct08.log 4.470e-01 7.670e-01
## `prdline.my.fctriPad 1` -7.310e-02 8.246e-01
## `prdline.my.fctriPad 2` 2.891e-02 8.408e-01
## `prdline.my.fctriPad 3+` 1.289e+00 7.401e-01
## prdline.my.fctriPadAir 7.113e-01 7.309e-01
## prdline.my.fctriPadmini 7.826e-01 7.464e-01
## `prdline.my.fctriPadmini 2+` -1.796e-01 8.493e-01
## color.fctrBlack 1.603e-01 2.806e-01
## color.fctrGold 3.446e-03 5.564e-01
## `color.fctrSpace Gray` -4.856e-01 3.629e-01
## color.fctrWhite -3.166e-01 2.702e-01
## D.npnct16.log 3.245e+00 1.813e+00
## D.nstopwrds.log 5.878e-01 6.650e-01
## D.npnct24.log 9.335e-01 2.451e+00
## D.npnct06.log -5.641e+00 2.155e+00
## D.npnct28.log -6.571e-02 2.189e+00
## D.nuppr.log 1.100e-03 4.852e-01
## D.nchrs.log -8.520e-02 4.869e-01
## D.npnct12.log -7.261e-02 7.655e-01
## D.nwrds.log -6.485e-02 7.787e-01
## D.npnct09.log -1.468e+00 5.527e+00
## D.ndgts.log 1.213e+00 4.751e-01
## D.terms.n.post.stop.log -2.345e-01 1.038e+00
## D.nwrds.unq.log -2.349e-01 1.041e+00
## D.terms.n.post.stem.log -2.349e-01 1.041e+00
## `carrier.fctrAT&T` -3.693e-01 7.938e-01
## carrier.fctrOther 4.414e-01 1.925e+00
## carrier.fctrSprint 6.126e-01 9.811e-01
## `carrier.fctrT-Mobile` -9.110e-01 1.094e+00
## carrier.fctrUnknown -5.054e-03 8.158e-01
## carrier.fctrVerizon 2.202e-01 8.307e-01
## cellular.fctr1 5.067e-01 7.570e-01
## cellular.fctrUnknown -5.872e-01 9.022e-01
## D.npnct14.log -2.197e+00 1.069e+00
## D.terms.n.post.stem -9.215e-02 2.074e-01
## D.terms.n.post.stop -1.183e-01 2.064e-01
## D.npnct05.log -4.422e+00 1.613e+00
## `condition.fctrFor parts or not working` 4.222e-02 4.071e-01
## `condition.fctrManufacturer refurbished` 5.571e-01 6.472e-01
## condition.fctrNew -2.090e-02 3.324e-01
## `condition.fctrNew other (see details)` 7.391e-01 5.066e-01
## `condition.fctrSeller refurbished` -4.597e-01 4.602e-01
## startprice.diff -8.917e-03 2.522e-03
## biddable 3.400e+00 5.238e-01
## idseq.my -4.535e-06 4.538e-04
## `prdline.my.fctriPad 1:startprice.diff` -5.851e-02 1.635e-02
## `prdline.my.fctriPad 2:startprice.diff` -9.755e-03 6.450e-03
## `prdline.my.fctriPad 3+:startprice.diff` -2.229e-02 5.823e-03
## `prdline.my.fctriPadAir:startprice.diff` -6.944e-03 3.739e-03
## `prdline.my.fctriPadmini:startprice.diff` -1.584e-02 5.655e-03
## `prdline.my.fctriPadmini 2+:startprice.diff` -1.247e-02 5.602e-03
## `prdline.my.fctriPad 1:biddable` 1.620e+00 8.937e-01
## `prdline.my.fctriPad 2:biddable` 1.004e+00 8.199e-01
## `prdline.my.fctriPad 3+:biddable` -1.235e-01 7.072e-01
## `prdline.my.fctriPadAir:biddable` 6.430e-01 7.028e-01
## `prdline.my.fctriPadmini:biddable` -4.107e-01 6.843e-01
## `prdline.my.fctriPadmini 2+:biddable` 2.419e-01 8.251e-01
## `prdline.my.fctriPad 1:idseq.my` 8.186e-04 6.593e-04
## `prdline.my.fctriPad 2:idseq.my` -6.048e-04 7.163e-04
## `prdline.my.fctriPad 3+:idseq.my` -3.408e-04 5.779e-04
## `prdline.my.fctriPadAir:idseq.my` -5.565e-04 6.021e-04
## `prdline.my.fctriPadmini:idseq.my` -1.389e-04 5.730e-04
## `prdline.my.fctriPadmini 2+:idseq.my` 6.848e-04 6.698e-04
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.456e+00 6.841e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 7.438e-02 7.928e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.084e-01 7.311e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.141e+00 7.946e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -8.994e-01 7.151e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 5.066e-01 7.071e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 6.345e-01 8.829e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -6.230e-01 8.507e-01
## `prdline.my.fctriPad 1:.clusterid.fctr3` -5.550e-01 1.037e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.506e+00 1.594e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 3.632e-02 6.731e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 2.203e-01 8.791e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 5.059e-01 7.914e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -6.296e-01 8.834e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000e+00 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.676e+00 1.109e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 8.446e-01 1.119e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 5.405e-01 8.151e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -5.591e-01 8.191e-01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -4.440e-01 8.072e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000e+00 2.500e+00
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.068e+00 1.286e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000e+00 2.500e+00
## `prdline.my.fctriPadmini:.clusterid.fctr5` -3.539e-01 1.181e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000e+00 2.500e+00
## z value Pr(>|z|)
## (Intercept) 0.264 0.791888
## D.ratio.nstopwrds.nwrds -0.684 0.494191
## D.npnct15.log 0.710 0.477616
## D.npnct03.log -0.421 0.673449
## D.terms.n.stem.stop.Ratio -0.126 0.899985
## D.ratio.sum.TfIdf.nwrds -1.010 0.312659
## .rnorm 0.255 0.798398
## D.npnct01.log 0.326 0.744226
## D.TfIdf.sum.stem.stop.Ratio -0.122 0.902775
## storage.fctr16 -1.340 0.180268
## storage.fctr32 -1.964 0.049501 *
## storage.fctr64 0.271 0.786335
## storage.fctrUnknown -1.733 0.083112 .
## D.npnct11.log 0.528 0.597272
## D.npnct10.log -1.106 0.268898
## D.TfIdf.sum.post.stop 0.149 0.881357
## D.TfIdf.sum.post.stem 0.219 0.826645
## D.sum.TfIdf 0.219 0.826645
## D.npnct13.log 0.448 0.653858
## D.npnct08.log 0.583 0.560079
## `prdline.my.fctriPad 1` -0.089 0.929362
## `prdline.my.fctriPad 2` 0.034 0.972571
## `prdline.my.fctriPad 3+` 1.742 0.081594 .
## prdline.my.fctriPadAir 0.973 0.330468
## prdline.my.fctriPadmini 1.049 0.294387
## `prdline.my.fctriPadmini 2+` -0.211 0.832551
## color.fctrBlack 0.571 0.567806
## color.fctrGold 0.006 0.995059
## `color.fctrSpace Gray` -1.338 0.180828
## color.fctrWhite -1.172 0.241305
## D.npnct16.log 1.790 0.073488 .
## D.nstopwrds.log 0.884 0.376742
## D.npnct24.log 0.381 0.703338
## D.npnct06.log -2.618 0.008839 **
## D.npnct28.log -0.030 0.976051
## D.nuppr.log 0.002 0.998191
## D.nchrs.log -0.175 0.861078
## D.npnct12.log -0.095 0.924437
## D.nwrds.log -0.083 0.933627
## D.npnct09.log -0.266 0.790532
## D.ndgts.log 2.552 0.010710 *
## D.terms.n.post.stop.log -0.226 0.821237
## D.nwrds.unq.log -0.226 0.821532
## D.terms.n.post.stem.log -0.226 0.821532
## `carrier.fctrAT&T` -0.465 0.641785
## carrier.fctrOther 0.229 0.818617
## carrier.fctrSprint 0.624 0.532373
## `carrier.fctrT-Mobile` -0.833 0.405083
## carrier.fctrUnknown -0.006 0.995057
## carrier.fctrVerizon 0.265 0.790942
## cellular.fctr1 0.669 0.503305
## cellular.fctrUnknown -0.651 0.515153
## D.npnct14.log -2.056 0.039826 *
## D.terms.n.post.stem -0.444 0.656781
## D.terms.n.post.stop -0.573 0.566623
## D.npnct05.log -2.741 0.006122 **
## `condition.fctrFor parts or not working` 0.104 0.917394
## `condition.fctrManufacturer refurbished` 0.861 0.389381
## condition.fctrNew -0.063 0.949856
## `condition.fctrNew other (see details)` 1.459 0.144539
## `condition.fctrSeller refurbished` -0.999 0.317863
## startprice.diff -3.536 0.000407 ***
## biddable 6.492 8.45e-11 ***
## idseq.my -0.010 0.992028
## `prdline.my.fctriPad 1:startprice.diff` -3.578 0.000346 ***
## `prdline.my.fctriPad 2:startprice.diff` -1.512 0.130424
## `prdline.my.fctriPad 3+:startprice.diff` -3.828 0.000129 ***
## `prdline.my.fctriPadAir:startprice.diff` -1.857 0.063292 .
## `prdline.my.fctriPadmini:startprice.diff` -2.801 0.005089 **
## `prdline.my.fctriPadmini 2+:startprice.diff` -2.226 0.026009 *
## `prdline.my.fctriPad 1:biddable` 1.813 0.069807 .
## `prdline.my.fctriPad 2:biddable` 1.224 0.220924
## `prdline.my.fctriPad 3+:biddable` -0.175 0.861339
## `prdline.my.fctriPadAir:biddable` 0.915 0.360222
## `prdline.my.fctriPadmini:biddable` -0.600 0.548408
## `prdline.my.fctriPadmini 2+:biddable` 0.293 0.769402
## `prdline.my.fctriPad 1:idseq.my` 1.242 0.214377
## `prdline.my.fctriPad 2:idseq.my` -0.844 0.398499
## `prdline.my.fctriPad 3+:idseq.my` -0.590 0.555397
## `prdline.my.fctriPadAir:idseq.my` -0.924 0.355383
## `prdline.my.fctriPadmini:idseq.my` -0.242 0.808430
## `prdline.my.fctriPadmini 2+:idseq.my` 1.022 0.306628
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.128 0.033320 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.094 0.925248
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.285 0.775566
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.437 0.150826
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.258 0.208509
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.716 0.473705
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.719 0.472327
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.732 0.463950
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.535 0.592577
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.945 0.344871
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.054 0.956972
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.251 0.802127
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.639 0.522654
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.713 0.476076
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.512 0.130547
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.755 0.450353
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.663 0.507301
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.683 0.494847
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.550 0.582309
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.386 0.017042 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.300 0.764378
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 663.15 on 864 degrees of freedom
## AIC: 883.15
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7506383
## 3 0.2 0.7932011
## 4 0.3 0.8130081
## 5 0.4 0.8304721
## 6 0.5 0.8388571
## 7 0.6 0.8285714
## 8 0.7 0.8119552
## 9 0.8 0.7629139
## 10 0.9 0.6160850
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.bayesglm.N
## 1 N 466
## 2 Y 83
## sold.fctr.predict.All.Interact.X.bayesglm.Y
## 1 58
## 2 367
## Prediction
## Reference N Y
## N 466 58
## Y 83 367
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.552361e-01 7.076445e-01 8.315533e-01 8.767468e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.056179e-98 4.326273e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7265774
## 3 0.2 0.7486744
## 4 0.3 0.7583815
## 5 0.4 0.7729469
## 6 0.5 0.7969543
## 7 0.6 0.7963206
## 8 0.7 0.7879617
## 9 0.8 0.7420290
## 10 0.9 0.5852843
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.bayesglm.N
## 1 N 411
## 2 Y 96
## sold.fctr.predict.All.Interact.X.bayesglm.Y
## 1 64
## 2 314
## Prediction
## Reference N Y
## N 411 64
## Y 96 314
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.192090e-01 6.345052e-01 7.922402e-01 8.440325e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 8.195907e-70 1.425529e-02
## model_id model_method
## 1 All.Interact.X.bayesglm bayesglm
## feats
## 1 D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.178 0.505
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9261281 0.5 0.8388571 0.8018519
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8315533 0.8767468 0.6010074 0.8673582
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7969543 0.819209
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7922402 0.8440325 0.6345052 883.1471
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01571542 0.0329967
## label step_major step_minor bgn end elapsed
## 8 fit.models_1_bayesglm 8 0 180.311 185.797 5.487
## 9 fit.models_1_glmnet 9 0 185.798 NA NA
## [1] "fitting model: All.Interact.X.glmnet"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha
## Length Class Mode
## a0 84 -none- numeric
## beta 9156 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 109 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -1.071851e+00
## D.npnct15.log
## 4.266444e-01
## .rnorm
## 1.214892e-03
## D.npnct01.log
## 2.808387e-03
## storage.fctr32
## -2.105252e-01
## storage.fctr64
## 5.311066e-01
## storage.fctrUnknown
## -3.327280e-01
## D.npnct10.log
## -2.018629e+00
## D.TfIdf.sum.post.stem
## 4.720688e-02
## D.sum.TfIdf
## 1.120480e-06
## prdline.my.fctriPad 3+
## 3.417411e-01
## color.fctrBlack
## 5.535054e-02
## color.fctrSpace Gray
## -2.672569e-01
## color.fctrWhite
## -1.809478e-01
## D.npnct06.log
## -1.117156e+00
## D.ndgts.log
## 2.439055e-01
## carrier.fctrSprint
## 6.559722e-01
## carrier.fctrT-Mobile
## -6.159378e-02
## carrier.fctrVerizon
## 2.519349e-01
## cellular.fctr1
## 3.291090e-02
## cellular.fctrUnknown
## -7.017078e-01
## D.npnct14.log
## -1.066797e+00
## D.terms.n.post.stem
## -1.874865e-02
## D.terms.n.post.stop
## -1.295554e-02
## D.npnct05.log
## -2.338872e+00
## condition.fctrManufacturer refurbished
## 2.620472e-01
## condition.fctrNew
## -4.809865e-02
## condition.fctrNew other (see details)
## 5.153368e-01
## condition.fctrSeller refurbished
## -2.045123e-01
## startprice.diff
## -9.922903e-03
## biddable
## 3.014829e+00
## idseq.my
## -6.267334e-05
## prdline.my.fctriPad 1:startprice.diff
## -3.474805e-02
## prdline.my.fctriPad 2:startprice.diff
## -3.560722e-03
## prdline.my.fctriPad 3+:startprice.diff
## -1.286821e-02
## prdline.my.fctriPadAir:startprice.diff
## -2.192992e-03
## prdline.my.fctriPadmini:startprice.diff
## -1.167589e-02
## prdline.my.fctriPadmini 2+:startprice.diff
## -5.030936e-03
## prdline.my.fctriPad 1:biddable
## 6.332994e-01
## prdline.my.fctriPad 2:biddable
## 3.204299e-01
## prdline.my.fctriPadAir:biddable
## 3.665535e-01
## prdline.my.fctriPad 1:idseq.my
## 4.511012e-04
## prdline.my.fctriPad 2:idseq.my
## -2.389369e-04
## prdline.my.fctriPadAir:idseq.my
## -5.458154e-05
## prdline.my.fctriPadmini 2+:idseq.my
## 7.910002e-05
## prdline.my.fctrUnknown:.clusterid.fctr2
## 9.185778e-01
## prdline.my.fctriPad 1:.clusterid.fctr2
## 1.515635e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -3.072119e-01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -6.552784e-01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 5.893832e-01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -7.160406e-01
## prdline.my.fctriPad 2:.clusterid.fctr3
## -1.709696e+00
## prdline.my.fctriPadmini:.clusterid.fctr3
## 5.629901e-01
## prdline.my.fctriPad 1:.clusterid.fctr4
## -1.005797e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 3.451929e-01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -1.281276e-01
## prdline.my.fctriPad 2:.clusterid.fctr5
## 2.647768e+00
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 7.078316e+00
## D.ratio.nstopwrds.nwrds
## -7.168457e+00
## D.npnct15.log
## 5.690381e-01
## D.npnct03.log
## -9.108520e-01
## D.terms.n.stem.stop.Ratio
## -8.929569e-02
## D.ratio.sum.TfIdf.nwrds
## -3.361056e-01
## .rnorm
## 2.595797e-02
## D.npnct01.log
## 1.643323e-01
## D.TfIdf.sum.stem.stop.Ratio
## -9.313161e-01
## storage.fctr16
## -8.878343e-01
## storage.fctr32
## -1.361324e+00
## storage.fctrUnknown
## -1.451838e+00
## D.npnct11.log
## 2.391393e-01
## D.npnct10.log
## -1.094287e+01
## D.TfIdf.sum.post.stem
## 1.816461e-01
## D.sum.TfIdf
## 5.320462e-03
## D.npnct13.log
## 1.709621e-01
## D.npnct08.log
## 5.605765e-01
## prdline.my.fctriPad 2
## 1.802617e-02
## prdline.my.fctriPad 3+
## 1.651397e+00
## prdline.my.fctriPadAir
## 1.005799e+00
## prdline.my.fctriPadmini
## 1.074416e+00
## color.fctrBlack
## 1.969397e-01
## color.fctrGold
## -2.622015e-02
## color.fctrSpace Gray
## -5.026492e-01
## color.fctrWhite
## -3.232513e-01
## D.npnct16.log
## 3.884682e+00
## D.nstopwrds.log
## 1.972564e+00
## D.npnct24.log
## 3.156937e-02
## D.npnct06.log
## -6.718634e+00
## D.npnct28.log
## -5.867155e-01
## D.nchrs.log
## -6.023664e-01
## D.npnct12.log
## -4.897941e-02
## D.npnct09.log
## -1.296182e+00
## D.ndgts.log
## 1.318937e+00
## D.nwrds.unq.log
## -2.161924e+00
## D.terms.n.post.stem.log
## -1.970537e-16
## carrier.fctrAT&T
## -4.735255e-01
## carrier.fctrOther
## 3.122673e+00
## carrier.fctrSprint
## 5.624587e-01
## carrier.fctrT-Mobile
## -1.219579e+00
## carrier.fctrVerizon
## 1.705070e-01
## cellular.fctr1
## 6.231476e-01
## cellular.fctrUnknown
## -5.515860e-01
## D.npnct14.log
## -2.490804e+00
## D.terms.n.post.stop
## -1.735829e-01
## D.npnct05.log
## -5.280322e+00
## condition.fctrFor parts or not working
## 9.580821e-03
## condition.fctrManufacturer refurbished
## 5.961320e-01
## condition.fctrNew other (see details)
## 8.042263e-01
## condition.fctrSeller refurbished
## -4.887843e-01
## startprice.diff
## -9.174971e-03
## biddable
## 3.544086e+00
## idseq.my
## 1.356103e-04
## prdline.my.fctriPad 1:startprice.diff
## -6.541284e-02
## prdline.my.fctriPad 2:startprice.diff
## -1.024689e-02
## prdline.my.fctriPad 3+:startprice.diff
## -2.416299e-02
## prdline.my.fctriPadAir:startprice.diff
## -7.486892e-03
## prdline.my.fctriPadmini:startprice.diff
## -1.604006e-02
## prdline.my.fctriPadmini 2+:startprice.diff
## -1.372973e-02
## prdline.my.fctriPad 1:biddable
## 2.157679e+00
## prdline.my.fctriPad 2:biddable
## 1.240603e+00
## prdline.my.fctriPad 3+:biddable
## -1.647133e-01
## prdline.my.fctriPadAir:biddable
## 6.499318e-01
## prdline.my.fctriPadmini:biddable
## -5.227249e-01
## prdline.my.fctriPadmini 2+:biddable
## 2.589720e-01
## prdline.my.fctriPad 1:idseq.my
## 8.352692e-04
## prdline.my.fctriPad 2:idseq.my
## -7.322703e-04
## prdline.my.fctriPad 3+:idseq.my
## -5.510381e-04
## prdline.my.fctriPadAir:idseq.my
## -7.348302e-04
## prdline.my.fctriPadmini:idseq.my
## -2.759999e-04
## prdline.my.fctriPadmini 2+:idseq.my
## 6.278549e-04
## prdline.my.fctrUnknown:.clusterid.fctr2
## 1.613582e+00
## prdline.my.fctriPad 1:.clusterid.fctr2
## -1.156028e-01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 2.678246e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.527531e+00
## prdline.my.fctriPadAir:.clusterid.fctr2
## -1.070915e+00
## prdline.my.fctriPadmini:.clusterid.fctr2
## 5.122784e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 7.101637e-01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -7.060338e-01
## prdline.my.fctriPad 1:.clusterid.fctr3
## -9.898297e-01
## prdline.my.fctriPad 2:.clusterid.fctr3
## -4.967315e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 1.697505e-02
## prdline.my.fctriPadAir:.clusterid.fctr3
## 1.805793e-01
## prdline.my.fctriPadmini:.clusterid.fctr3
## 4.263241e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -8.796400e-01
## prdline.my.fctriPad 1:.clusterid.fctr4
## -2.313679e+00
## prdline.my.fctriPad 2:.clusterid.fctr4
## 1.290711e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 6.482265e-01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -9.678674e-01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -6.430547e-01
## prdline.my.fctriPad 2:.clusterid.fctr5
## 3.826005e+00
## prdline.my.fctriPadmini:.clusterid.fctr5
## -6.192454e-01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7148594
## 3 0.2 0.7788899
## 4 0.3 0.7928287
## 5 0.4 0.8131148
## 6 0.5 0.8227115
## 7 0.6 0.8140948
## 8 0.7 0.8025316
## 9 0.8 0.7553763
## 10 0.9 0.5123153
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.glmnet.N
## 1 N 466
## 2 Y 95
## sold.fctr.predict.All.Interact.X.glmnet.Y
## 1 58
## 2 355
## Prediction
## Reference N Y
## N 466 58
## Y 95 355
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.429158e-01 6.821623e-01 8.185193e-01 8.652175e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.699210e-90 3.609347e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7054611
## 3 0.2 0.7454545
## 4 0.3 0.7581552
## 5 0.4 0.7811366
## 6 0.5 0.7861716
## 7 0.6 0.7930108
## 8 0.7 0.7844228
## 9 0.8 0.7318519
## 10 0.9 0.5098039
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.glmnet.N
## 1 N 436
## 2 Y 115
## sold.fctr.predict.All.Interact.X.glmnet.Y
## 1 39
## 2 295
## Prediction
## Reference N Y
## N 436 39
## Y 115 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.259887e-01 6.455950e-01 7.993726e-01 8.504130e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.992247e-73 1.506454e-09
## model_id model_method
## 1 All.Interact.X.glmnet glmnet
## feats
## 1 D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 6.26 1.46
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9132994 0.5 0.8227115 0.8038936
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8185193 0.8652175 0.6039602 0.8728883
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7930108 0.8259887
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7993726 0.850413 0.645595
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02105445 0.04398531
## label step_major step_minor bgn end elapsed
## 9 fit.models_1_glmnet 9 0 185.798 196.112 10.314
## 10 fit.models_1_rpart 10 0 196.112 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rpart"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00833 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.148888889 1 0.4888889
## 3 0.008333333 2 0.3400000
##
## Variable importance
## biddable
## 34
## startprice.diff
## 21
## idseq.my
## 7
## prdline.my.fctriPad 3+:biddable
## 6
## prdline.my.fctriPadAir:biddable
## 6
## prdline.my.fctriPad 1:biddable
## 5
## prdline.my.fctriPadmini:biddable
## 5
## prdline.my.fctriPadAir:startprice.diff
## 5
## prdline.my.fctriPad 3+:startprice.diff
## 4
## prdline.my.fctriPadmini 2+:startprice.diff
## 3
## prdline.my.fctriPadmini:startprice.diff
## 3
## prdline.my.fctriPad 2:startprice.diff
## 2
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 44.18097 to the right, improve= 78.49785, (0 missing)
## idseq.my < 905.5 to the right, improve= 38.91299, (0 missing)
## prdline.my.fctriPad 1:biddable < 0.5 to the left, improve= 25.37925, (0 missing)
## prdline.my.fctriPad 2:biddable < 0.5 to the left, improve= 22.83949, (0 missing)
## Surrogate splits:
## idseq.my < 869 to the right, agree=0.636, adj=0.211, (0 split)
## prdline.my.fctriPad 3+:biddable < 0.5 to the left, agree=0.624, adj=0.187, (0 split)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, agree=0.618, adj=0.173, (0 split)
## prdline.my.fctriPad 1:biddable < 0.5 to the left, agree=0.613, adj=0.162, (0 split)
## prdline.my.fctriPadmini:biddable < 0.5 to the left, agree=0.611, adj=0.158, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.1488889
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (89 obs) right son=7 (361 obs)
## Primary splits:
## startprice.diff < 84.72197 to the right, improve=88.61445, (0 missing)
## prdline.my.fctriPad 3+:startprice.diff < 90.53641 to the right, improve=20.17141, (0 missing)
## idseq.my < 670.5 to the right, improve=15.11062, (0 missing)
## prdline.my.fctriPadmini:startprice.diff < 54.43284 to the right, improve=13.52563, (0 missing)
## prdline.my.fctriPadAir:startprice.diff < 102.8707 to the right, improve=11.78578, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir:startprice.diff < 92.45617 to the right, agree=0.851, adj=0.247, (0 split)
## prdline.my.fctriPad 3+:startprice.diff < 90.53641 to the right, agree=0.840, adj=0.191, (0 split)
## prdline.my.fctriPadmini:startprice.diff < 88.75737 to the right, agree=0.827, adj=0.124, (0 split)
## prdline.my.fctriPadmini 2+:startprice.diff < 105.0949 to the right, agree=0.827, adj=0.124, (0 split)
## prdline.my.fctriPad 2:startprice.diff < 76.28875 to the right, agree=0.818, adj=0.079, (0 split)
##
## Node number 6: 89 observations
## predicted class=N expected loss=0.1235955 P(node) =0.09137577
## class counts: 78 11
## probabilities: 0.876 0.124
##
## Node number 7: 361 observations
## predicted class=Y expected loss=0.08864266 P(node) =0.3706366
## class counts: 32 329
## probabilities: 0.089 0.911
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=84.72197 89 11 N (0.87640449 0.12359551) *
## 7) startprice.diff< 84.72197 361 32 Y (0.08864266 0.91135734) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6576779
## 4 0.3 0.8113440
## 5 0.4 0.8113440
## 6 0.5 0.8113440
## 7 0.6 0.8113440
## 8 0.7 0.8113440
## 9 0.8 0.8113440
## 10 0.9 0.8113440
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rpart.N
## 1 N 492
## 2 Y 121
## sold.fctr.predict.All.Interact.X.no.rnorm.rpart.Y
## 1 32
## 2 329
## Prediction
## Reference N Y
## N 492 32
## Y 121 329
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.429158e-01 6.795322e-01 8.185193e-01 8.652175e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.699210e-90 1.124184e-12
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6515397
## 4 0.3 0.7934783
## 5 0.4 0.7934783
## 6 0.5 0.7934783
## 7 0.6 0.7934783
## 8 0.7 0.7934783
## 9 0.8 0.7934783
## 10 0.9 0.7934783
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rpart.N
## 1 N 441
## 2 Y 118
## sold.fctr.predict.All.Interact.X.no.rnorm.rpart.Y
## 1 34
## 2 292
## Prediction
## Reference N Y
## N 441 34
## Y 118 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.282486e-01 6.497240e-01 8.017531e-01 8.525367e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.168374e-74 1.671294e-11
## model_id model_method
## 1 All.Interact.X.no.rnorm.rpart rpart
## feats
## 1 D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.739 0.077
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8435581 0.9 0.811344 0.8347135
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8185193 0.8652175 0.6642538 0.8273068
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.7934783 0.8282486
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8017531 0.8525367 0.649724
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0167898 0.03471308
## label step_major step_minor bgn end elapsed
## 10 fit.models_1_rpart 10 0 196.112 201.596 5.484
## 11 fit.models_1_rf 11 0 201.596 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rf"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 55 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 108 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 108 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.8547009
## 3 0.2 0.9564293
## 4 0.3 0.9836066
## 5 0.4 1.0000000
## 6 0.5 1.0000000
## 7 0.6 0.9988877
## 8 0.7 0.9547038
## 9 0.8 0.8916256
## 10 0.9 0.8110964
## 11 1.0 0.3240223
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.N
## 1 N 524
## 2 Y NA
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.Y
## 1 NA
## 2 450
## Prediction
## Reference N Y
## N 524 0
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.962198e-01 1.000000e+00 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.919016e-263 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7174312
## 3 0.2 0.7621951
## 4 0.3 0.7900677
## 5 0.4 0.7980535
## 6 0.5 0.8120104
## 7 0.6 0.8102981
## 8 0.7 0.8027586
## 9 0.8 0.7823613
## 10 0.9 0.7346327
## 11 1.0 0.1890110
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.N
## 1 N 430
## 2 Y 99
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.Y
## 1 45
## 2 311
## Prediction
## Reference N Y
## N 430 45
## Y 99 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.372881e-01 6.698360e-01 8.112909e-01 8.610159e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.003894e-79 1.002346e-05
## model_id model_method
## 1 All.Interact.X.no.rnorm.rf rf
## feats
## 1 D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 18.267 5.426
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.8460019
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9962198 1 0.6873959 0.8910295
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.8120104 0.8372881
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8112909 0.8610159 0.669836
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01330961 0.0282581# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
model_id <- "csm"; indep_vars_vctr <- c(NULL
,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
,"prdline.my.fctr*biddable"
#,"prdline.my.fctr*startprice.log"
,"prdline.my.fctr*startprice.diff"
#,"prdline.my.fctr*idseq.my"
,"prdline.my.fctr*condition.fctr"
,"prdline.my.fctr*D.terms.n.post.stop"
#,"prdline.my.fctr*D.terms.n.post.stem"
,"prdline.my.fctr*cellular.fctr"
# ,"<feat1>:<feat2>"
)
for (method in glb_models_method_vctr) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".",
method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: csm.glm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 639
## Warning: not plotting observations with leverage one:
## 639
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.76431 -0.52933 -0.04817 0.40570 2.57496
##
## Coefficients: (11 not defined because of singularities)
## Estimate
## (Intercept) -2.035e+00
## `prdline.my.fctriPad 1` 1.046e+00
## `prdline.my.fctriPad 2` 2.661e-01
## `prdline.my.fctriPad 3+` 1.239e+00
## prdline.my.fctriPadAir 8.554e-01
## prdline.my.fctriPadmini 1.272e+00
## `prdline.my.fctriPadmini 2+` 1.434e-01
## biddable 3.476e+00
## startprice.diff -8.746e-03
## `condition.fctrFor parts or not working` 1.440e+00
## `condition.fctrManufacturer refurbished` -9.262e+00
## condition.fctrNew -6.758e-01
## `condition.fctrNew other (see details)` 5.175e+00
## `condition.fctrSeller refurbished` 4.722e-01
## D.terms.n.post.stop 2.414e-02
## cellular.fctr1 -3.107e+00
## cellular.fctrUnknown -1.003e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.483e+00
## `prdline.my.fctriPad 1:.clusterid.fctr2` -2.709e+00
## `prdline.my.fctriPad 2:.clusterid.fctr2` 6.468e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -3.660e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -8.400e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.657e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.852e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.168e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -4.385e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.490e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.261e+00
## `prdline.my.fctriPadAir:.clusterid.fctr3` 5.776e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.815e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 2.023e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -5.155e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.078e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.158e+00
## `prdline.my.fctriPadAir:.clusterid.fctr4` -2.270e-01
## `prdline.my.fctriPadmini:.clusterid.fctr4` 1.178e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 4.610e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.237e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 2.092e+00
## `prdline.my.fctriPad 2:biddable` 1.063e+00
## `prdline.my.fctriPad 3+:biddable` 2.489e-01
## `prdline.my.fctriPadAir:biddable` 6.338e-01
## `prdline.my.fctriPadmini:biddable` 3.826e-02
## `prdline.my.fctriPadmini 2+:biddable` 2.768e-01
## `prdline.my.fctriPad 1:startprice.diff` -5.542e-02
## `prdline.my.fctriPad 2:startprice.diff` -1.179e-02
## `prdline.my.fctriPad 3+:startprice.diff` -2.315e-02
## `prdline.my.fctriPadAir:startprice.diff` -6.637e-03
## `prdline.my.fctriPadmini:startprice.diff` -1.977e-02
## `prdline.my.fctriPadmini 2+:startprice.diff` -1.436e-02
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -8.422e-01
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -3.149e-01
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.248e+00
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -1.677e+00
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -2.741e+00
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.698e+01
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 8.534e+00
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.042e+01
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 8.778e+00
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 1:condition.fctrNew` -1.457e+01
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -7.033e-01
## `prdline.my.fctriPadAir:condition.fctrNew` 5.886e-01
## `prdline.my.fctriPadmini:condition.fctrNew` -2.294e-01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.846e+00
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -6.127e+00
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -1.841e+01
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -3.559e+00
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -3.472e+00
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -5.390e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -6.100e+00
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.413e+00
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -8.394e-01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -1.968e+00
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -1.827e+00
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -1.260e+00
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -1.707e+01
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 3.172e-01
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.071e-01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.653e-01
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -8.350e-02
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -2.173e-01
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -2.491e-01
## `prdline.my.fctriPad 1:cellular.fctr1` 2.937e+00
## `prdline.my.fctriPad 2:cellular.fctr1` 2.226e+00
## `prdline.my.fctriPad 3+:cellular.fctr1` 4.269e+00
## `prdline.my.fctriPadAir:cellular.fctr1` 2.851e+00
## `prdline.my.fctriPadmini:cellular.fctr1` 4.286e+00
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 4.006e+00
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 1.111e-01
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -2.989e-01
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -1.248e+00
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 4.163e-01
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 3.140e-02
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 4.144e-01
## Std. Error
## (Intercept) 7.560e-01
## `prdline.my.fctriPad 1` 9.139e-01
## `prdline.my.fctriPad 2` 1.013e+00
## `prdline.my.fctriPad 3+` 9.746e-01
## prdline.my.fctriPadAir 8.750e-01
## prdline.my.fctriPadmini 8.877e-01
## `prdline.my.fctriPadmini 2+` 1.112e+00
## biddable 7.883e-01
## startprice.diff 3.046e-03
## `condition.fctrFor parts or not working` 8.787e-01
## `condition.fctrManufacturer refurbished` 2.400e+03
## condition.fctrNew 1.002e+00
## `condition.fctrNew other (see details)` 2.064e+00
## `condition.fctrSeller refurbished` 1.201e+00
## D.terms.n.post.stop 9.542e-02
## cellular.fctr1 1.829e+00
## cellular.fctrUnknown 8.432e-01
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.415e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.816e+00
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.095e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 9.718e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 9.535e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.011e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 2.047e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.004e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` 2.189e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` 7.721e+02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 9.377e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 1.021e+00
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.239e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.571e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.185e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.313e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.054e+00
## `prdline.my.fctriPadAir:.clusterid.fctr4` 9.756e-01
## `prdline.my.fctriPadmini:.clusterid.fctr4` 1.195e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.714e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.654e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 1.412e+00
## `prdline.my.fctriPad 2:biddable` 1.128e+00
## `prdline.my.fctriPad 3+:biddable` 1.082e+00
## `prdline.my.fctriPadAir:biddable` 9.745e-01
## `prdline.my.fctriPadmini:biddable` 1.012e+00
## `prdline.my.fctriPadmini 2+:biddable` 1.220e+00
## `prdline.my.fctriPad 1:startprice.diff` 2.014e-02
## `prdline.my.fctriPad 2:startprice.diff` 7.430e-03
## `prdline.my.fctriPad 3+:startprice.diff` 6.785e-03
## `prdline.my.fctriPadAir:startprice.diff` 4.352e-03
## `prdline.my.fctriPadmini:startprice.diff` 6.836e-03
## `prdline.my.fctriPadmini 2+:startprice.diff` 7.584e-03
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.866e+00
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 1.577e+00
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 1.246e+00
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 1.443e+00
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 1.122e+00
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 1.605e+03
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 2.400e+03
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 2.400e+03
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 2.400e+03
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 1:condition.fctrNew` 9.728e+02
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.876e+00
## `prdline.my.fctriPadAir:condition.fctrNew` 1.168e+00
## `prdline.my.fctriPadmini:condition.fctrNew` 1.222e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.355e+00
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 1.131e+01
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 1.639e+03
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 2.402e+00
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 2.283e+00
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 2.486e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 2.436e+00
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.845e+00
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 1.616e+00
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 1.609e+00
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 1.699e+00
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.648e+00
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 1.553e+03
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 2.319e-01
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 1.472e-01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 1.286e-01
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 1.290e-01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 1.400e-01
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 2.067e-01
## `prdline.my.fctriPad 1:cellular.fctr1` 1.995e+00
## `prdline.my.fctriPad 2:cellular.fctr1` 1.960e+00
## `prdline.my.fctriPad 3+:cellular.fctr1` 1.951e+00
## `prdline.my.fctriPadAir:cellular.fctr1` 1.896e+00
## `prdline.my.fctriPadmini:cellular.fctr1` 1.943e+00
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 2.011e+00
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 1.734e+00
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 1.636e+00
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 1.473e+00
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 1.542e+00
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 1.395e+00
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 1.423e+00
## z value
## (Intercept) -2.692
## `prdline.my.fctriPad 1` 1.145
## `prdline.my.fctriPad 2` 0.263
## `prdline.my.fctriPad 3+` 1.271
## prdline.my.fctriPadAir 0.978
## prdline.my.fctriPadmini 1.433
## `prdline.my.fctriPadmini 2+` 0.129
## biddable 4.410
## startprice.diff -2.871
## `condition.fctrFor parts or not working` 1.639
## `condition.fctrManufacturer refurbished` -0.004
## condition.fctrNew -0.674
## `condition.fctrNew other (see details)` 2.507
## `condition.fctrSeller refurbished` 0.393
## D.terms.n.post.stop 0.253
## cellular.fctr1 -1.698
## cellular.fctrUnknown -1.190
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.575
## `prdline.my.fctriPad 1:.clusterid.fctr2` -1.492
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.591
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.377
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.881
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.639
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.882
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.163
## `prdline.my.fctriPad 1:.clusterid.fctr3` -2.003
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.019
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.345
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.566
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.465
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.288
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.359
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.821
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 2.048
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.233
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.986
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.689
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.748
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 1.482
## `prdline.my.fctriPad 2:biddable` 0.942
## `prdline.my.fctriPad 3+:biddable` 0.230
## `prdline.my.fctriPadAir:biddable` 0.650
## `prdline.my.fctriPadmini:biddable` 0.038
## `prdline.my.fctriPadmini 2+:biddable` 0.227
## `prdline.my.fctriPad 1:startprice.diff` -2.751
## `prdline.my.fctriPad 2:startprice.diff` -1.587
## `prdline.my.fctriPad 3+:startprice.diff` -3.412
## `prdline.my.fctriPadAir:startprice.diff` -1.525
## `prdline.my.fctriPadmini:startprice.diff` -2.891
## `prdline.my.fctriPadmini 2+:startprice.diff` -1.893
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` -0.451
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -0.200
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.001
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -1.162
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -2.442
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -0.011
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.004
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.004
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.004
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 1:condition.fctrNew` -0.015
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.375
## `prdline.my.fctriPadAir:condition.fctrNew` 0.504
## `prdline.my.fctriPadmini:condition.fctrNew` -0.188
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.362
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.542
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.011
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -1.482
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -1.521
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -2.168
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -2.504
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.766
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.519
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -1.223
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -1.075
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -0.764
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -0.011
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.368
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.728
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.286
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.647
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.553
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -1.205
## `prdline.my.fctriPad 1:cellular.fctr1` 1.472
## `prdline.my.fctriPad 2:cellular.fctr1` 1.136
## `prdline.my.fctriPad 3+:cellular.fctr1` 2.188
## `prdline.my.fctriPadAir:cellular.fctr1` 1.504
## `prdline.my.fctriPadmini:cellular.fctr1` 2.205
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.992
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.064
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.183
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -0.847
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.270
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.023
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.291
## Pr(>|z|)
## (Intercept) 0.007113
## `prdline.my.fctriPad 1` 0.252327
## `prdline.my.fctriPad 2` 0.792761
## `prdline.my.fctriPad 3+` 0.203593
## prdline.my.fctriPadAir 0.328276
## prdline.my.fctriPadmini 0.151795
## `prdline.my.fctriPadmini 2+` 0.897343
## biddable 1.03e-05
## startprice.diff 0.004088
## `condition.fctrFor parts or not working` 0.101160
## `condition.fctrManufacturer refurbished` 0.996920
## condition.fctrNew 0.500182
## `condition.fctrNew other (see details)` 0.012163
## `condition.fctrSeller refurbished` 0.694049
## D.terms.n.post.stop 0.800242
## cellular.fctr1 0.089444
## cellular.fctrUnknown 0.234174
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.115163
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.135767
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.554619
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.706447
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.378363
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.101253
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.059817
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.244730
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.045200
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.984602
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.178660
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.571428
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.143021
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.197917
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.018327
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.411512
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.040580
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.816047
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.324192
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.007160
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.454692
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.138469
## `prdline.my.fctriPad 2:biddable` 0.345937
## `prdline.my.fctriPad 3+:biddable` 0.817971
## `prdline.my.fctriPadAir:biddable` 0.515431
## `prdline.my.fctriPadmini:biddable` 0.969838
## `prdline.my.fctriPadmini 2+:biddable` 0.820546
## `prdline.my.fctriPad 1:startprice.diff` 0.005940
## `prdline.my.fctriPad 2:startprice.diff` 0.112538
## `prdline.my.fctriPad 3+:startprice.diff` 0.000644
## `prdline.my.fctriPadAir:startprice.diff` 0.127290
## `prdline.my.fctriPadmini:startprice.diff` 0.003835
## `prdline.my.fctriPadmini 2+:startprice.diff` 0.058353
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.651767
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.841694
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.316727
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.245243
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.014588
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.991560
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.997162
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.996534
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.997081
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 1:condition.fctrNew` 0.988053
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.707766
## `prdline.my.fctriPadAir:condition.fctrNew` 0.614162
## `prdline.my.fctriPadmini:condition.fctrNew` 0.851006
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.173161
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.587992
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.991039
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.138473
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.128287
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.030155
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.012278
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.443753
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.603479
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.221386
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.282153
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.444620
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.991226
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.171232
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.466912
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.198525
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.517338
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.120517
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.228181
## `prdline.my.fctriPad 1:cellular.fctr1` 0.141054
## `prdline.my.fctriPad 2:cellular.fctr1` 0.256041
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.028653
## `prdline.my.fctriPadAir:cellular.fctr1` 0.132563
## `prdline.my.fctriPadmini:cellular.fctr1` 0.027421
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.046347
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.948929
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.855006
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.397119
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.787098
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.982047
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.770914
##
## (Intercept) **
## `prdline.my.fctriPad 1`
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## biddable ***
## startprice.diff **
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew
## `condition.fctrNew other (see details)` *
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1 .
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2`
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` .
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3` *
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4` *
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4` *
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5` **
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable`
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable`
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:startprice.diff` **
## `prdline.my.fctriPad 2:startprice.diff`
## `prdline.my.fctriPad 3+:startprice.diff` ***
## `prdline.my.fctriPadAir:startprice.diff`
## `prdline.my.fctriPadmini:startprice.diff` **
## `prdline.my.fctriPadmini 2+:startprice.diff` .
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` *
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` *
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` *
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1` *
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1` *
## `prdline.my.fctriPadmini 2+:cellular.fctr1` *
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown`
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 662.45 on 880 degrees of freedom
## AIC: 850.45
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7489289
## 3 0.2 0.7973485
## 4 0.3 0.8207739
## 5 0.4 0.8315098
## 6 0.5 0.8293242
## 7 0.6 0.8219833
## 8 0.7 0.8114144
## 9 0.8 0.7720685
## 10 0.9 0.6725146
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.glm.N sold.fctr.predict.csm.glm.Y
## 1 N 440 84
## 2 Y 70 380
## Prediction
## Reference N Y
## N 440 84
## Y 70 380
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.418891e-01 6.826395e-01 8.174354e-01 8.642544e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 7.796162e-90 2.948368e-01
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7240705
## 3 0.2 0.7231760
## 4 0.3 0.7459954
## 5 0.4 0.7588739
## 6 0.5 0.7747748
## 7 0.6 0.7708609
## 8 0.7 0.7651934
## 9 0.8 0.7357664
## 10 0.9 0.6215781
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.glm.N sold.fctr.predict.csm.glm.Y
## 1 N 409 66
## 2 Y 109 301
## Prediction
## Reference N Y
## N 409 66
## Y 109 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.022599e-01 5.995009e-01 7.744658e-01 8.280245e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 2.734441e-61 1.498873e-03
## model_id model_method
## 1 csm.glm glm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.908 0.306
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9246141 0.4 0.8315098 0.7884995
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8174354 0.8642544 0.5731278 0.8425315
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7747748 0.8022599
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7744658 0.8280245 0.5995009 850.4544
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02311165 0.04919951
## importance
## biddable 100.00000
## `prdline.my.fctriPad 3+:startprice.diff` 77.35573
## `prdline.my.fctriPadmini:startprice.diff` 65.53601
## startprice.diff 65.07762
## `prdline.my.fctriPad 1:startprice.diff` 62.35076
## `prdline.my.fctriPad 2:.clusterid.fctr5` 60.94899
## [1] "fitting model: csm.bayesglm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7170 -0.5760 -0.1076 0.4494 2.5294
##
## Coefficients:
## Estimate
## (Intercept) -1.697239
## `prdline.my.fctriPad 1` 0.684140
## `prdline.my.fctriPad 2` 0.051201
## `prdline.my.fctriPad 3+` 1.005200
## prdline.my.fctriPadAir 0.559909
## prdline.my.fctriPadmini 0.919847
## `prdline.my.fctriPadmini 2+` 0.056866
## biddable 3.080858
## startprice.diff -0.007684
## `condition.fctrFor parts or not working` 0.694025
## `condition.fctrManufacturer refurbished` 0.055555
## condition.fctrNew -0.789406
## `condition.fctrNew other (see details)` 1.349096
## `condition.fctrSeller refurbished` -0.157919
## D.terms.n.post.stop -0.007054
## cellular.fctr1 -0.519582
## cellular.fctrUnknown -0.692060
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.118960
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.605038
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.387043
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.381145
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.713444
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.053034
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.712572
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.924431
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.700088
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.903595
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.870240
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.437486
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.137744
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.702140
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.500196
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.674542
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.589031
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.170348
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.439091
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.739868
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.479564
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPad 1:biddable` 1.457478
## `prdline.my.fctriPad 2:biddable` 1.199837
## `prdline.my.fctriPad 3+:biddable` 0.353597
## `prdline.my.fctriPadAir:biddable` 0.879184
## `prdline.my.fctriPadmini:biddable` 0.282618
## `prdline.my.fctriPadmini 2+:biddable` 0.282838
## `prdline.my.fctriPad 1:startprice.diff` -0.046162
## `prdline.my.fctriPad 2:startprice.diff` -0.011907
## `prdline.my.fctriPad 3+:startprice.diff` -0.021792
## `prdline.my.fctriPadAir:startprice.diff` -0.006889
## `prdline.my.fctriPadmini:startprice.diff` -0.019541
## `prdline.my.fctriPadmini 2+:startprice.diff` -0.011557
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.851523
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.243017
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.460135
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.677646
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -1.829299
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.358968
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.000000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.000000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.490155
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.928555
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` -0.302015
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.013675
## `prdline.my.fctriPad 1:condition.fctrNew` -0.954618
## `prdline.my.fctriPad 2:condition.fctrNew` 0.000000
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.390219
## `prdline.my.fctriPadAir:condition.fctrNew` 0.641402
## `prdline.my.fctriPadmini:condition.fctrNew` -0.039291
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.632545
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.272098
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.754397
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.205759
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.242861
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -1.048826
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -1.779094
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.483235
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.100222
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.948700
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.821376
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -0.434114
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -1.476240
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.128276
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.053472
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.109832
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.058330
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.121570
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.073843
## `prdline.my.fctriPad 1:cellular.fctr1` 0.419077
## `prdline.my.fctriPad 2:cellular.fctr1` -0.269887
## `prdline.my.fctriPad 3+:cellular.fctr1` 1.396357
## `prdline.my.fctriPadAir:cellular.fctr1` 0.254954
## `prdline.my.fctriPadmini:cellular.fctr1` 1.565677
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.121080
## `prdline.my.fctriPad 1:cellular.fctrUnknown` -0.261280
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.386424
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -1.237846
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.043509
## `prdline.my.fctriPadmini:cellular.fctrUnknown` -0.204704
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.171147
## Std. Error
## (Intercept) 0.481851
## `prdline.my.fctriPad 1` 0.636489
## `prdline.my.fctriPad 2` 0.698731
## `prdline.my.fctriPad 3+` 0.678021
## prdline.my.fctriPadAir 0.602916
## prdline.my.fctriPadmini 0.614141
## `prdline.my.fctriPadmini 2+` 0.744443
## biddable 0.502050
## startprice.diff 0.002443
## `condition.fctrFor parts or not working` 0.574141
## `condition.fctrManufacturer refurbished` 1.097392
## condition.fctrNew 0.634270
## `condition.fctrNew other (see details)` 0.822832
## `condition.fctrSeller refurbished` 0.702033
## D.terms.n.post.stop 0.071795
## cellular.fctr1 0.623109
## cellular.fctrUnknown 0.494478
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.710742
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.056533
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.852760
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.799252
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.802609
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.791639
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.330145
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.776345
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.193393
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.614892
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.775188
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.857349
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.941158
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.093154
## `prdline.my.fctrUnknown:.clusterid.fctr4` 2.500000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.221426
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.019980
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.866804
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.823779
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.941393
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 2.500000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.294451
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.218561
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPad 1:biddable` 0.869928
## `prdline.my.fctriPad 2:biddable` 0.803394
## `prdline.my.fctriPad 3+:biddable` 0.745641
## `prdline.my.fctriPadAir:biddable` 0.693995
## `prdline.my.fctriPadmini:biddable` 0.724271
## `prdline.my.fctriPadmini 2+:biddable` 0.826072
## `prdline.my.fctriPad 1:startprice.diff` 0.015521
## `prdline.my.fctriPad 2:startprice.diff` 0.006586
## `prdline.my.fctriPad 3+:startprice.diff` 0.005872
## `prdline.my.fctriPadAir:startprice.diff` 0.003679
## `prdline.my.fctriPadmini:startprice.diff` 0.006162
## `prdline.my.fctriPadmini 2+:startprice.diff` 0.005776
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.160022
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 1.112469
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.899186
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 1.048309
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.819605
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 1.660205
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 2.500000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 2.500000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 1.297445
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.274120
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 1.286510
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 2.466752
## `prdline.my.fctriPad 1:condition.fctrNew` 1.709697
## `prdline.my.fctriPad 2:condition.fctrNew` 2.500000
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.273124
## `prdline.my.fctriPadAir:condition.fctrNew` 0.798404
## `prdline.my.fctriPadmini:condition.fctrNew` 0.845211
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.903726
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 2.053091
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 1.772790
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 1.161911
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 1.076915
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 1.246030
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.223977
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.233811
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 1.051818
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 1.056615
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 1.141599
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.082284
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 1.644347
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.139444
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.114026
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.101317
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.102509
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.106125
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.149909
## `prdline.my.fctriPad 1:cellular.fctr1` 0.840281
## `prdline.my.fctriPad 2:cellular.fctr1` 0.830056
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.809916
## `prdline.my.fctriPadAir:cellular.fctr1` 0.747653
## `prdline.my.fctriPadmini:cellular.fctr1` 0.829877
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.906864
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 1.177299
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 1.137571
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 1.042588
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 1.090862
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 1.011288
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 1.018200
## z value
## (Intercept) -3.522
## `prdline.my.fctriPad 1` 1.075
## `prdline.my.fctriPad 2` 0.073
## `prdline.my.fctriPad 3+` 1.483
## prdline.my.fctriPadAir 0.929
## prdline.my.fctriPadmini 1.498
## `prdline.my.fctriPadmini 2+` 0.076
## biddable 6.137
## startprice.diff -3.146
## `condition.fctrFor parts or not working` 1.209
## `condition.fctrManufacturer refurbished` 0.051
## condition.fctrNew -1.245
## `condition.fctrNew other (see details)` 1.640
## `condition.fctrSeller refurbished` -0.225
## D.terms.n.post.stop -0.098
## cellular.fctr1 -0.834
## cellular.fctrUnknown -1.400
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.574
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.573
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.454
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.477
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.889
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.330
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.288
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.191
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.425
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.179
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.123
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.510
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.209
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.642
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.047
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.661
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.833
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.207
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.466
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.889
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.394
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:biddable` 1.675
## `prdline.my.fctriPad 2:biddable` 1.493
## `prdline.my.fctriPad 3+:biddable` 0.474
## `prdline.my.fctriPadAir:biddable` 1.267
## `prdline.my.fctriPadmini:biddable` 0.390
## `prdline.my.fctriPadmini 2+:biddable` 0.342
## `prdline.my.fctriPad 1:startprice.diff` -2.974
## `prdline.my.fctriPad 2:startprice.diff` -1.808
## `prdline.my.fctriPad 3+:startprice.diff` -3.711
## `prdline.my.fctriPadAir:startprice.diff` -1.872
## `prdline.my.fctriPadmini:startprice.diff` -3.171
## `prdline.my.fctriPadmini 2+:startprice.diff` -2.001
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.734
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.218
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.512
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.646
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -2.232
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -0.819
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.378
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.729
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` -0.235
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.006
## `prdline.my.fctriPad 1:condition.fctrNew` -0.558
## `prdline.my.fctriPad 2:condition.fctrNew` 0.000
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.307
## `prdline.my.fctriPadAir:condition.fctrNew` 0.803
## `prdline.my.fctriPadmini:condition.fctrNew` -0.046
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.806
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.133
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.426
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.177
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.226
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -0.842
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -1.454
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.202
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.095
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.898
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.719
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -0.401
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -0.898
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.920
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.469
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.084
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.569
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -1.146
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.493
## `prdline.my.fctriPad 1:cellular.fctr1` 0.499
## `prdline.my.fctriPad 2:cellular.fctr1` -0.325
## `prdline.my.fctriPad 3+:cellular.fctr1` 1.724
## `prdline.my.fctriPadAir:cellular.fctr1` 0.341
## `prdline.my.fctriPadmini:cellular.fctr1` 1.887
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.236
## `prdline.my.fctriPad 1:cellular.fctrUnknown` -0.222
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.340
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -1.187
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.040
## `prdline.my.fctriPadmini:cellular.fctrUnknown` -0.202
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.168
## Pr(>|z|)
## (Intercept) 0.000428
## `prdline.my.fctriPad 1` 0.282435
## `prdline.my.fctriPad 2` 0.941585
## `prdline.my.fctriPad 3+` 0.138194
## prdline.my.fctriPadAir 0.353061
## prdline.my.fctriPadmini 0.134191
## `prdline.my.fctriPadmini 2+` 0.939111
## biddable 8.43e-10
## startprice.diff 0.001656
## `condition.fctrFor parts or not working` 0.226738
## `condition.fctrManufacturer refurbished` 0.959624
## condition.fctrNew 0.213283
## `condition.fctrNew other (see details)` 0.101093
## `condition.fctrSeller refurbished` 0.822021
## D.terms.n.post.stop 0.921731
## cellular.fctr1 0.404364
## cellular.fctrUnknown 0.161639
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.115405
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.566872
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.649921
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.633449
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.374053
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.183454
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.197917
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.233753
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.154278
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.238488
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.261600
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.609857
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.226710
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.520674
## `prdline.my.fctrUnknown:.clusterid.fctr4` 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.040663
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.508401
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.066772
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.836175
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.640910
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.003863
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.693914
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 1:biddable` 0.093855
## `prdline.my.fctriPad 2:biddable` 0.135317
## `prdline.my.fctriPad 3+:biddable` 0.635344
## `prdline.my.fctriPadAir:biddable` 0.205211
## `prdline.my.fctriPadmini:biddable` 0.696381
## `prdline.my.fctriPadmini 2+:biddable` 0.732058
## `prdline.my.fctriPad 1:startprice.diff` 0.002939
## `prdline.my.fctriPad 2:startprice.diff` 0.070635
## `prdline.my.fctriPad 3+:startprice.diff` 0.000206
## `prdline.my.fctriPadAir:startprice.diff` 0.061156
## `prdline.my.fctriPadmini:startprice.diff` 0.001519
## `prdline.my.fctriPadmini 2+:startprice.diff` 0.045391
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.462914
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.827080
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.608844
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.518009
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.025620
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.413041
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 1.000000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 1.000000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.705590
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.466136
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.814399
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.995577
## `prdline.my.fctriPad 1:condition.fctrNew` 0.576602
## `prdline.my.fctriPad 2:condition.fctrNew` 1.000000
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.759220
## `prdline.my.fctriPadAir:condition.fctrNew` 0.421770
## `prdline.my.fctriPadmini:condition.fctrNew` 0.962922
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.070847
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.894564
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.670442
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.859440
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.821578
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.399937
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.146075
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.229303
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.924089
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.369256
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.471835
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.688340
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.369310
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.357621
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.639110
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.278347
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.569338
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.251985
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.622305
## `prdline.my.fctriPad 1:cellular.fctr1` 0.617966
## `prdline.my.fctriPad 2:cellular.fctr1` 0.745073
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.084694
## `prdline.my.fctriPadAir:cellular.fctr1` 0.733099
## `prdline.my.fctriPadmini:cellular.fctr1` 0.059209
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.216378
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.824367
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.734088
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.235116
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.968185
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.839589
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.866515
##
## (Intercept) ***
## `prdline.my.fctriPad 1`
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## biddable ***
## startprice.diff **
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2`
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4` *
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4` .
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5` **
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable` .
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable`
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:startprice.diff` **
## `prdline.my.fctriPad 2:startprice.diff` .
## `prdline.my.fctriPad 3+:startprice.diff` ***
## `prdline.my.fctriPadAir:startprice.diff` .
## `prdline.my.fctriPadmini:startprice.diff` **
## `prdline.my.fctriPadmini 2+:startprice.diff` *
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` *
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew` .
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1` .
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1` .
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown`
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 677.67 on 869 degrees of freedom
## AIC: 887.67
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7327227
## 3 0.2 0.7876520
## 4 0.3 0.8134557
## 5 0.4 0.8311404
## 6 0.5 0.8252874
## 7 0.6 0.8210024
## 8 0.7 0.8085106
## 9 0.8 0.7547170
## 10 0.9 0.6345865
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.bayesglm.N
## 1 N 441
## 2 Y 71
## sold.fctr.predict.csm.bayesglm.Y
## 1 83
## 2 379
## Prediction
## Reference N Y
## N 441 83
## Y 71 379
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.418891e-01 6.825401e-01 8.174354e-01 8.642544e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 7.796162e-90 3.753992e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7272727
## 3 0.2 0.7439410
## 4 0.3 0.7659091
## 5 0.4 0.7849332
## 6 0.5 0.7893401
## 7 0.6 0.7973684
## 8 0.7 0.7867036
## 9 0.8 0.7368421
## 10 0.9 0.5966667
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.bayesglm.N
## 1 N 428
## 2 Y 107
## sold.fctr.predict.csm.bayesglm.Y
## 1 47
## 2 303
## Prediction
## Reference N Y
## N 428 47
## Y 107 303
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.259887e-01 6.465508e-01 7.993726e-01 8.504130e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.992247e-73 1.990805e-06
## model_id model_method
## 1 csm.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.398 0.451
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9218321 0.4 0.8311404 0.8059576
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8174354 0.8642544 0.6084395 0.8751682
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7973684 0.8259887
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7993726 0.850413 0.6465508 887.6748
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02013466 0.04297055
## N importance
## biddable 100.000000 100.000000
## startprice.diff 74.468483 74.468483
## prdline.my.fctr 34.103141 34.103141
## .clusterid.fctr 12.101557 12.101557
## D.terms.n.post.stop 6.618312 6.618312
## cellular.fctr 5.114660 5.114660
## [1] "fitting model: csm.glmnet"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 10400 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 104 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -0.785126522
## prdline.my.fctriPad 1
## 0.094864050
## prdline.my.fctriPad 3+
## 0.132318552
## biddable
## 1.657612909
## startprice.diff
## -0.005101683
## condition.fctrFor parts or not working
## 0.051728598
## condition.fctrNew
## -0.280672613
## condition.fctrNew other (see details)
## 0.083501269
## condition.fctrSeller refurbished
## -0.102950458
## D.terms.n.post.stop
## -0.019918996
## cellular.fctrUnknown
## -0.344710052
## prdline.my.fctrUnknown:.clusterid.fctr2
## 0.333916029
## prdline.my.fctriPad 1:.clusterid.fctr2
## 0.254154885
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -0.189166781
## prdline.my.fctriPadAir:.clusterid.fctr2
## -0.293689519
## prdline.my.fctriPadmini:.clusterid.fctr2
## 0.241456856
## prdline.my.fctrUnknown:.clusterid.fctr3
## -0.542249391
## prdline.my.fctriPad 2:.clusterid.fctr3
## -1.351549327
## prdline.my.fctriPadmini:.clusterid.fctr3
## 0.325734603
## prdline.my.fctriPad 1:.clusterid.fctr4
## -0.816555954
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 0.447582131
## prdline.my.fctriPadAir:.clusterid.fctr4
## -0.031134475
## prdline.my.fctriPadmini:.clusterid.fctr4
## -0.031709207
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.624126021
## prdline.my.fctriPad 1:biddable
## 0.868744927
## prdline.my.fctriPad 2:biddable
## 0.867484185
## prdline.my.fctriPad 3+:biddable
## 0.532596792
## prdline.my.fctriPadAir:biddable
## 0.871716398
## prdline.my.fctriPadmini:biddable
## 0.517827280
## prdline.my.fctriPadmini 2+:biddable
## 0.357321439
## prdline.my.fctriPad 1:startprice.diff
## -0.014585870
## prdline.my.fctriPad 2:startprice.diff
## -0.003913650
## prdline.my.fctriPad 3+:startprice.diff
## -0.007533428
## prdline.my.fctriPadAir:startprice.diff
## -0.002981635
## prdline.my.fctriPadmini:startprice.diff
## -0.008857312
## prdline.my.fctriPadmini 2+:startprice.diff
## -0.003308179
## prdline.my.fctriPad 1:condition.fctrFor parts or not working
## 1.325865083
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 0.150099097
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -0.380551440
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -0.887424697
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## 0.393103261
## prdline.my.fctriPad 1:condition.fctrNew
## -0.551635876
## prdline.my.fctriPad 3+:condition.fctrNew
## -0.148551661
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 0.064426169
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -0.403291726
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 0.602952762
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## 0.340466958
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## -0.051791987
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## -0.592682408
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## 0.913759705
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## -0.396171064
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -0.229905145
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## -0.258933947
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished
## -1.136087129
## prdline.my.fctriPad 1:D.terms.n.post.stop
## 0.010744828
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -0.022428598
## prdline.my.fctriPadAir:D.terms.n.post.stop
## -0.019761785
## prdline.my.fctriPad 2:cellular.fctr1
## -0.352872696
## prdline.my.fctriPad 3+:cellular.fctr1
## 0.171655188
## prdline.my.fctriPadAir:cellular.fctr1
## -0.104655936
## prdline.my.fctriPadmini:cellular.fctr1
## 0.426749577
## prdline.my.fctriPad 2:cellular.fctrUnknown
## -0.343388715
## prdline.my.fctriPad 3+:cellular.fctrUnknown
## -0.599203821
## prdline.my.fctriPadmini:cellular.fctrUnknown
## -0.108462300
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -1.908097e+00
## prdline.my.fctriPad 1
## 9.232560e-01
## prdline.my.fctriPad 2
## 1.448326e-01
## prdline.my.fctriPad 3+
## 1.118741e+00
## prdline.my.fctriPadAir
## 7.269006e-01
## prdline.my.fctriPadmini
## 1.139041e+00
## prdline.my.fctriPadmini 2+
## 4.467221e-02
## biddable
## 3.220985e+00
## startprice.diff
## -8.122717e-03
## condition.fctrFor parts or not working
## 1.279546e+00
## condition.fctrManufacturer refurbished
## -5.342958e-05
## condition.fctrNew
## -7.649789e-01
## condition.fctrNew other (see details)
## 4.292654e+00
## condition.fctrSeller refurbished
## 4.381645e-01
## D.terms.n.post.stop
## 1.054294e-02
## cellular.fctr1
## -2.090343e+00
## cellular.fctrUnknown
## -8.354327e-01
## prdline.my.fctrUnknown:.clusterid.fctr2
## 1.299903e+00
## prdline.my.fctriPad 1:.clusterid.fctr2
## -2.375268e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## 6.198774e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -3.787361e-01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -8.324500e-01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.617222e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.656829e+00
## prdline.my.fctrUnknown:.clusterid.fctr3
## -1.066430e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## -3.971714e+00
## prdline.my.fctriPad 2:.clusterid.fctr3
## -5.139483e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 1.215954e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## 5.705059e-01
## prdline.my.fctriPadmini:.clusterid.fctr3
## 1.771634e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## 1.906954e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## -4.760242e+00
## prdline.my.fctriPad 2:.clusterid.fctr4
## 1.055194e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 2.105022e+00
## prdline.my.fctriPadAir:.clusterid.fctr4
## -2.281940e-01
## prdline.my.fctriPadmini:.clusterid.fctr4
## 1.125813e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 4.556858e+00
## prdline.my.fctriPadmini:.clusterid.fctr5
## 1.186111e+00
## prdline.my.fctriPad 1:biddable
## 2.166591e+00
## prdline.my.fctriPad 2:biddable
## 1.301508e+00
## prdline.my.fctriPad 3+:biddable
## 4.684927e-01
## prdline.my.fctriPadAir:biddable
## 8.779222e-01
## prdline.my.fctriPadmini:biddable
## 2.760250e-01
## prdline.my.fctriPadmini 2+:biddable
## 4.829626e-01
## prdline.my.fctriPad 1:startprice.diff
## -5.325816e-02
## prdline.my.fctriPad 2:startprice.diff
## -1.227553e-02
## prdline.my.fctriPad 3+:startprice.diff
## -2.327921e-02
## prdline.my.fctriPadAir:startprice.diff
## -7.176673e-03
## prdline.my.fctriPadmini:startprice.diff
## -2.013394e-02
## prdline.my.fctriPadmini 2+:startprice.diff
## -1.442104e-02
## prdline.my.fctriPad 1:condition.fctrFor parts or not working
## -4.704473e-01
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## -1.496551e-01
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -1.085197e+00
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -1.504022e+00
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -2.564908e+00
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -6.463523e+00
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -7.064960e-01
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## 1.159809e+00
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## -4.682701e-01
## prdline.my.fctriPad 1:condition.fctrNew
## -4.939381e+00
## prdline.my.fctriPad 3+:condition.fctrNew
## -5.891387e-01
## prdline.my.fctriPadAir:condition.fctrNew
## 6.771964e-01
## prdline.my.fctriPadmini:condition.fctrNew
## -1.263002e-01
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 1.897538e+00
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -4.851107e+00
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -7.094134e+00
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## -2.659737e+00
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## -2.584542e+00
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## -4.492406e+00
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## -5.199392e+00
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## 1.446224e+00
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## -7.936695e-01
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## -1.910234e+00
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -1.773017e+00
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## -1.217800e+00
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished
## -6.713347e+00
## prdline.my.fctriPad 1:D.terms.n.post.stop
## 2.923420e-01
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -9.017609e-02
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -1.478048e-01
## prdline.my.fctriPadAir:D.terms.n.post.stop
## -6.942920e-02
## prdline.my.fctriPadmini:D.terms.n.post.stop
## -1.980418e-01
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## -2.208570e-01
## prdline.my.fctriPad 1:cellular.fctr1
## 1.927842e+00
## prdline.my.fctriPad 2:cellular.fctr1
## 1.203964e+00
## prdline.my.fctriPad 3+:cellular.fctr1
## 3.211938e+00
## prdline.my.fctriPadAir:cellular.fctr1
## 1.830077e+00
## prdline.my.fctriPadmini:cellular.fctr1
## 3.253492e+00
## prdline.my.fctriPadmini 2+:cellular.fctr1
## 2.953754e+00
## prdline.my.fctriPad 1:cellular.fctrUnknown
## -8.405316e-02
## prdline.my.fctriPad 2:cellular.fctrUnknown
## -4.649041e-01
## prdline.my.fctriPad 3+:cellular.fctrUnknown
## -1.397003e+00
## prdline.my.fctriPadAir:cellular.fctrUnknown
## 2.442932e-01
## prdline.my.fctriPadmini:cellular.fctrUnknown
## -1.249146e-01
## prdline.my.fctriPadmini 2+:cellular.fctrUnknown
## 2.555720e-01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6671609
## 3 0.2 0.7199341
## 4 0.3 0.7785235
## 5 0.4 0.7986871
## 6 0.5 0.8098016
## 7 0.6 0.8123457
## 8 0.7 0.7635403
## 9 0.8 0.5975232
## 10 0.9 0.1247401
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.glmnet.N sold.fctr.predict.csm.glmnet.Y
## 1 N 493 31
## 2 Y 121 329
## Prediction
## Reference N Y
## N 493 31
## Y 121 329
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.439425e-01 6.815761e-01 8.196035e-01 8.661802e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.675019e-91 5.242780e-13
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6710744
## 3 0.2 0.7191217
## 4 0.3 0.7550802
## 5 0.4 0.7711138
## 6 0.5 0.7802341
## 7 0.6 0.7925033
## 8 0.7 0.7334315
## 9 0.8 0.5752961
## 10 0.9 0.1431767
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.glmnet.N sold.fctr.predict.csm.glmnet.Y
## 1 N 434 41
## 2 Y 114 296
## Prediction
## Reference N Y
## N 434 41
## Y 114 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.248588e-01 6.434744e-01 7.981829e-01 8.493505e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 8.130489e-73 7.330192e-09
## model_id model_method
## 1 csm.glmnet glmnet
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 3.011 0.356
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.908626 0.6 0.8123457 0.8080089
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8196035 0.8661802 0.6119731 0.8701669
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7925033 0.8248588
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7981829 0.8493505 0.6434744
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01385701 0.03112462
## importance
## biddable 100.00000
## prdline.my.fctriPad 2:.clusterid.fctr5 98.96605
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 89.00671
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 75.34252
## prdline.my.fctriPadAir:biddable 73.91272
## prdline.my.fctriPad 1:biddable 73.81033
## [1] "fitting model: csm.rpart"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00833 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.148888889 1 0.4888889
## 3 0.008333333 2 0.3400000
##
## Variable importance
## biddable
## 34
## startprice.diff
## 21
## prdline.my.fctriPad 3+:biddable
## 6
## prdline.my.fctriPadAir:biddable
## 6
## prdline.my.fctriPad 1:biddable
## 6
## prdline.my.fctriPadmini:biddable
## 5
## prdline.my.fctriPadAir:startprice.diff
## 5
## prdline.my.fctriPad 2:biddable
## 5
## prdline.my.fctriPad 3+:startprice.diff
## 4
## prdline.my.fctriPadmini 2+:startprice.diff
## 3
## prdline.my.fctriPadmini:startprice.diff
## 3
## prdline.my.fctriPad 2:startprice.diff
## 2
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 44.18097 to the right, improve= 78.49785, (0 missing)
## prdline.my.fctriPad 1:biddable < 0.5 to the left, improve= 25.37925, (0 missing)
## prdline.my.fctriPad 2:biddable < 0.5 to the left, improve= 22.83949, (0 missing)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, improve= 16.00553, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 3+:biddable < 0.5 to the left, agree=0.624, adj=0.187, (0 split)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, agree=0.618, adj=0.173, (0 split)
## prdline.my.fctriPad 1:biddable < 0.5 to the left, agree=0.613, adj=0.162, (0 split)
## prdline.my.fctriPadmini:biddable < 0.5 to the left, agree=0.611, adj=0.158, (0 split)
## prdline.my.fctriPad 2:biddable < 0.5 to the left, agree=0.606, adj=0.147, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.1488889
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (89 obs) right son=7 (361 obs)
## Primary splits:
## startprice.diff < 84.72197 to the right, improve=88.61445, (0 missing)
## prdline.my.fctriPad 3+:startprice.diff < 90.53641 to the right, improve=20.17141, (0 missing)
## prdline.my.fctriPadmini:startprice.diff < 54.43284 to the right, improve=13.52563, (0 missing)
## prdline.my.fctriPadAir:startprice.diff < 102.8707 to the right, improve=11.78578, (0 missing)
## prdline.my.fctriPadmini 2+:startprice.diff < 138.451 to the right, improve=11.67677, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir:startprice.diff < 92.45617 to the right, agree=0.851, adj=0.247, (0 split)
## prdline.my.fctriPad 3+:startprice.diff < 90.53641 to the right, agree=0.840, adj=0.191, (0 split)
## prdline.my.fctriPadmini:startprice.diff < 88.75737 to the right, agree=0.827, adj=0.124, (0 split)
## prdline.my.fctriPadmini 2+:startprice.diff < 105.0949 to the right, agree=0.827, adj=0.124, (0 split)
## prdline.my.fctriPad 2:startprice.diff < 76.28875 to the right, agree=0.818, adj=0.079, (0 split)
##
## Node number 6: 89 observations
## predicted class=N expected loss=0.1235955 P(node) =0.09137577
## class counts: 78 11
## probabilities: 0.876 0.124
##
## Node number 7: 361 observations
## predicted class=Y expected loss=0.08864266 P(node) =0.3706366
## class counts: 32 329
## probabilities: 0.089 0.911
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=84.72197 89 11 N (0.87640449 0.12359551) *
## 7) startprice.diff< 84.72197 361 32 Y (0.08864266 0.91135734) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6576779
## 4 0.3 0.8113440
## 5 0.4 0.8113440
## 6 0.5 0.8113440
## 7 0.6 0.8113440
## 8 0.7 0.8113440
## 9 0.8 0.8113440
## 10 0.9 0.8113440
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.rpart.N sold.fctr.predict.csm.rpart.Y
## 1 N 492 32
## 2 Y 121 329
## Prediction
## Reference N Y
## N 492 32
## Y 121 329
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.429158e-01 6.795322e-01 8.185193e-01 8.652175e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.699210e-90 1.124184e-12
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6515397
## 4 0.3 0.7934783
## 5 0.4 0.7934783
## 6 0.5 0.7934783
## 7 0.6 0.7934783
## 8 0.7 0.7934783
## 9 0.8 0.7934783
## 10 0.9 0.7934783
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.rpart.N sold.fctr.predict.csm.rpart.Y
## 1 N 441 34
## 2 Y 118 292
## Prediction
## Reference N Y
## N 441 34
## Y 118 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.282486e-01 6.497240e-01 8.017531e-01 8.525367e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.168374e-74 1.671294e-11
## model_id model_method
## 1 csm.rpart rpart
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.792 0.094
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8435581 0.9 0.811344 0.841912
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8185193 0.8652175 0.6771481 0.8273068
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.7934783 0.8282486
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8017531 0.8525367 0.649724
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02468137 0.0529597
## importance
## startprice.diff 100.00000
## biddable 86.25929
## prdline.my.fctriPad 1:biddable 15.18694
## prdline.my.fctriPad 2:biddable 13.66715
## prdline.my.fctriPad 3+:startprice.diff 12.07057
## prdline.my.fctriPadAir:biddable 9.57771
## [1] "fitting model: csm.rf"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 53 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 104 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 104 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.8628955
## 3 0.2 0.9384776
## 4 0.3 0.9761388
## 5 0.4 0.9966777
## 6 0.5 0.9988901
## 7 0.6 0.9910314
## 8 0.7 0.9374262
## 9 0.8 0.8805970
## 10 0.9 0.8235294
## 11 1.0 0.5123967
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.rf.N sold.fctr.predict.csm.rf.Y
## 1 N 523 1
## 2 Y NA 450
## Prediction
## Reference N Y
## N 523 1
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.989733e-01 9.979350e-01 9.942930e-01 9.999740e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 4.956882e-260 1.000000e+00
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.7381890
## 3 0.2 0.7705443
## 4 0.3 0.7981962
## 5 0.4 0.8080094
## 6 0.5 0.8184080
## 7 0.6 0.8125000
## 8 0.7 0.7896879
## 9 0.8 0.7752809
## 10 0.9 0.7543860
## 11 1.0 0.3922330
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.rf.N sold.fctr.predict.csm.rf.Y
## 1 N 410 65
## 2 Y 81 329
## Prediction
## Reference N Y
## N 410 65
## Y 81 329
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.350282e-01 6.673789e-01 8.089040e-01 8.588985e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.946649e-78 2.144548e-01
## model_id model_method
## 1 csm.rf rf
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 16.848 5.689
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9999915 0.5 0.9988901 0.8305983
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.994293 0.999974 0.6567546 0.8875302
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.818408 0.8350282
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.808904 0.8588985 0.6673789
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02013977 0.04149673
## importance
## startprice.diff 100.000000
## biddable 88.327674
## prdline.my.fctriPadAir:startprice.diff 11.945378
## D.terms.n.post.stop 9.579481
## prdline.my.fctriPad 3+:startprice.diff 8.795106
## prdline.my.fctriPadmini:startprice.diff 8.522285# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Max.cor.Y.glm Max.cor.Y.glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm
## Low.cor.X.glm Low.cor.X.glm
## All.X.glm All.X.glm
## All.X.bayesglm All.X.bayesglm
## All.X.glmnet All.X.glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf
## All.Interact.X.glm All.Interact.X.glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf
## csm.glm csm.glm
## csm.bayesglm csm.bayesglm
## csm.glmnet csm.glmnet
## csm.rpart csm.rpart
## csm.rf csm.rf
## model_method
## MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart rpart
## Max.cor.Y.glm glm
## Interact.High.cor.Y.glm glm
## Low.cor.X.glm glm
## All.X.glm glm
## All.X.bayesglm bayesglm
## All.X.glmnet glmnet
## All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf rf
## All.Interact.X.glm glm
## All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf rf
## csm.glm glm
## csm.bayesglm bayesglm
## csm.glmnet glmnet
## csm.rpart rpart
## csm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.diff
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.diff
## Max.cor.Y.rpart biddable, startprice.diff
## Max.cor.Y.glm biddable, startprice.diff
## Interact.High.cor.Y.glm biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.417
## Random.myrandom_classfr 0 0.282
## Max.cor.Y.cv.0.rpart 0 0.634
## Max.cor.Y.cv.0.cp.0.rpart 0 0.480
## Max.cor.Y.rpart 3 1.057
## Max.cor.Y.glm 1 0.971
## Interact.High.cor.Y.glm 1 1.050
## Low.cor.X.glm 1 1.386
## All.X.glm 1 1.571
## All.X.bayesglm 1 2.587
## All.X.glmnet 9 5.217
## All.X.no.rnorm.rpart 3 1.673
## All.X.no.rnorm.rf 3 15.404
## All.Interact.X.glm 1 1.836
## All.Interact.X.bayesglm 1 2.178
## All.Interact.X.glmnet 9 6.260
## All.Interact.X.no.rnorm.rpart 3 1.739
## All.Interact.X.no.rnorm.rf 3 18.267
## csm.glm 1 1.908
## csm.bayesglm 1 2.398
## csm.glmnet 9 3.011
## csm.rpart 3 1.792
## csm.rf 3 16.848
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.003 0.5000000
## Random.myrandom_classfr 0.002 0.5071756
## Max.cor.Y.cv.0.rpart 0.012 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.009 0.9051018
## Max.cor.Y.rpart 0.011 0.8435581
## Max.cor.Y.glm 0.013 0.8706955
## Interact.High.cor.Y.glm 0.015 0.8716794
## Low.cor.X.glm 0.168 0.9063401
## All.X.glm 0.250 0.9130577
## All.X.bayesglm 0.365 0.9103774
## All.X.glmnet 0.961 0.8773155
## All.X.no.rnorm.rpart 0.063 0.8435581
## All.X.no.rnorm.rf 4.801 1.0000000
## All.Interact.X.glm 0.373 0.9285072
## All.Interact.X.bayesglm 0.505 0.9261281
## All.Interact.X.glmnet 1.460 0.9132994
## All.Interact.X.no.rnorm.rpart 0.077 0.8435581
## All.Interact.X.no.rnorm.rf 5.426 1.0000000
## csm.glm 0.306 0.9246141
## csm.bayesglm 0.451 0.9218321
## csm.glmnet 0.356 0.9086260
## csm.rpart 0.094 0.8435581
## csm.rf 5.689 0.9999915
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320225
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.8538283
## Max.cor.Y.rpart 0.9 0.8113440
## Max.cor.Y.glm 0.7 0.7906404
## Interact.High.cor.Y.glm 0.7 0.7915633
## Low.cor.X.glm 0.5 0.8123570
## All.X.glm 0.4 0.8217391
## All.X.bayesglm 0.6 0.8238095
## All.X.glmnet 0.6 0.8048485
## All.X.no.rnorm.rpart 0.9 0.8113440
## All.X.no.rnorm.rf 0.6 1.0000000
## All.Interact.X.glm 0.4 0.8396125
## All.Interact.X.bayesglm 0.5 0.8388571
## All.Interact.X.glmnet 0.5 0.8227115
## All.Interact.X.no.rnorm.rpart 0.9 0.8113440
## All.Interact.X.no.rnorm.rf 0.5 1.0000000
## csm.glm 0.4 0.8315098
## csm.bayesglm 0.4 0.8311404
## csm.glmnet 0.6 0.8123457
## csm.rpart 0.9 0.8113440
## csm.rf 0.5 0.9988901
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.5379877 0.5060896
## Random.myrandom_classfr 0.4620123 0.4303445
## Max.cor.Y.cv.0.rpart 0.5379877 0.5060896
## Max.cor.Y.cv.0.cp.0.rpart 0.8706366 0.8479228
## Max.cor.Y.rpart 0.8367648 0.8185193
## Max.cor.Y.glm 0.7977525 0.8001376
## Interact.High.cor.Y.glm 0.7936467 0.8022954
## Low.cor.X.glm 0.7854194 0.8066148
## All.X.glm 0.7905476 0.8066148
## All.X.bayesglm 0.7946660 0.8239439
## All.X.glmnet 0.7977588 0.8098576
## All.X.no.rnorm.rpart 0.8347135 0.8185193
## All.X.no.rnorm.rf 0.8418803 0.9962198
## All.Interact.X.glm 0.7844033 0.8228583
## All.Interact.X.bayesglm 0.8018519 0.8315533
## All.Interact.X.glmnet 0.8038936 0.8185193
## All.Interact.X.no.rnorm.rpart 0.8347135 0.8185193
## All.Interact.X.no.rnorm.rf 0.8460019 0.9962198
## csm.glm 0.7884995 0.8174354
## csm.bayesglm 0.8059576 0.8174354
## csm.glmnet 0.8080089 0.8196035
## csm.rpart 0.8419120 0.8185193
## csm.rf 0.8305983 0.9942930
## max.AccuracyUpper.fit max.Kappa.fit
## MFO.myMFO_classfr 0.5696555 0.0000000
## Random.myrandom_classfr 0.4939104 0.0000000
## Max.cor.Y.cv.0.rpart 0.5696555 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8910807 0.7382100
## Max.cor.Y.rpart 0.8652175 0.6685851
## Max.cor.Y.glm 0.8488005 0.5911469
## Interact.High.cor.Y.glm 0.8507367 0.5830988
## Low.cor.X.glm 0.8546053 0.5673801
## All.X.glm 0.8546053 0.5768469
## All.X.bayesglm 0.8700277 0.5859868
## All.X.glmnet 0.8575034 0.5910839
## All.X.no.rnorm.rpart 0.8652175 0.6642538
## All.X.no.rnorm.rf 1.0000000 0.6791071
## All.Interact.X.glm 0.8690663 0.5662735
## All.Interact.X.bayesglm 0.8767468 0.6010074
## All.Interact.X.glmnet 0.8652175 0.6039602
## All.Interact.X.no.rnorm.rpart 0.8652175 0.6642538
## All.Interact.X.no.rnorm.rf 1.0000000 0.6873959
## csm.glm 0.8642544 0.5731278
## csm.bayesglm 0.8642544 0.6084395
## csm.glmnet 0.8661802 0.6119731
## csm.rpart 0.8652175 0.6771481
## csm.rf 0.9999740 0.6567546
## max.auc.OOB opt.prob.threshold.OOB
## MFO.myMFO_classfr 0.5000000 0.5
## Random.myrandom_classfr 0.5191913 0.4
## Max.cor.Y.cv.0.rpart 0.5000000 0.5
## Max.cor.Y.cv.0.cp.0.rpart 0.8812811 0.6
## Max.cor.Y.rpart 0.8273068 0.9
## Max.cor.Y.glm 0.8658999 0.7
## Interact.High.cor.Y.glm 0.8652888 0.7
## Low.cor.X.glm 0.8499512 0.7
## All.X.glm 0.8492837 0.6
## All.X.bayesglm 0.8576175 0.6
## All.X.glmnet 0.8631682 0.6
## All.X.no.rnorm.rpart 0.8273068 0.9
## All.X.no.rnorm.rf 0.8876893 0.6
## All.Interact.X.glm 0.8559230 0.6
## All.Interact.X.bayesglm 0.8673582 0.5
## All.Interact.X.glmnet 0.8728883 0.6
## All.Interact.X.no.rnorm.rpart 0.8273068 0.9
## All.Interact.X.no.rnorm.rf 0.8910295 0.5
## csm.glm 0.8425315 0.5
## csm.bayesglm 0.8751682 0.6
## csm.glmnet 0.8701669 0.6
## csm.rpart 0.8273068 0.9
## csm.rf 0.8875302 0.5
## max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr 0.0000000 0.5367232
## Random.myrandom_classfr 0.6332046 0.4632768
## Max.cor.Y.cv.0.rpart 0.0000000 0.5367232
## Max.cor.Y.cv.0.cp.0.rpart 0.7994859 0.8237288
## Max.cor.Y.rpart 0.7934783 0.8282486
## Max.cor.Y.glm 0.7896175 0.8259887
## Interact.High.cor.Y.glm 0.7868852 0.8237288
## Low.cor.X.glm 0.7765668 0.8146893
## All.X.glm 0.7769029 0.8079096
## All.X.bayesglm 0.7830688 0.8146893
## All.X.glmnet 0.7962213 0.8293785
## All.X.no.rnorm.rpart 0.7934783 0.8282486
## All.X.no.rnorm.rf 0.8086839 0.8406780
## All.Interact.X.glm 0.7894737 0.8192090
## All.Interact.X.bayesglm 0.7969543 0.8192090
## All.Interact.X.glmnet 0.7930108 0.8259887
## All.Interact.X.no.rnorm.rpart 0.7934783 0.8282486
## All.Interact.X.no.rnorm.rf 0.8120104 0.8372881
## csm.glm 0.7747748 0.8022599
## csm.bayesglm 0.7973684 0.8259887
## csm.glmnet 0.7925033 0.8248588
## csm.rpart 0.7934783 0.8282486
## csm.rf 0.8184080 0.8350282
## max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO.myMFO_classfr 0.5032294 0.5699717
## Random.myrandom_classfr 0.4300283 0.4967706
## Max.cor.Y.cv.0.rpart 0.5032294 0.5699717
## Max.cor.Y.cv.0.cp.0.rpart 0.7969937 0.8482877
## Max.cor.Y.rpart 0.8017531 0.8525367
## Max.cor.Y.glm 0.7993726 0.8504130
## Interact.High.cor.Y.glm 0.7969937 0.8482877
## Low.cor.X.glm 0.7874927 0.8397715
## All.X.glm 0.7803820 0.8333692
## All.X.bayesglm 0.7874927 0.8397715
## All.X.glmnet 0.8029439 0.8535980
## All.X.no.rnorm.rpart 0.8017531 0.8525367
## All.X.no.rnorm.rf 0.8148743 0.8641887
## All.Interact.X.glm 0.7922402 0.8440325
## All.Interact.X.bayesglm 0.7922402 0.8440325
## All.Interact.X.glmnet 0.7993726 0.8504130
## All.Interact.X.no.rnorm.rpart 0.8017531 0.8525367
## All.Interact.X.no.rnorm.rf 0.8112909 0.8610159
## csm.glm 0.7744658 0.8280245
## csm.bayesglm 0.7993726 0.8504130
## csm.glmnet 0.7981829 0.8493505
## csm.rpart 0.8017531 0.8525367
## csm.rf 0.8089040 0.8588985
## max.Kappa.OOB max.AccuracySD.fit
## MFO.myMFO_classfr 0.0000000 NA
## Random.myrandom_classfr 0.0000000 NA
## Max.cor.Y.cv.0.rpart 0.0000000 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.6430437 NA
## Max.cor.Y.rpart 0.6497240 0.015852866
## Max.cor.Y.glm 0.6448747 0.009555267
## Interact.High.cor.Y.glm 0.6402627 0.010313351
## Low.cor.X.glm 0.6219426 0.004787468
## All.X.glm 0.6099603 0.013569077
## All.X.bayesglm 0.6233456 0.004364881
## All.X.glmnet 0.6523227 0.015475235
## All.X.no.rnorm.rpart 0.6497240 0.016789805
## All.X.no.rnorm.rf 0.6751279 0.017072636
## All.Interact.X.glm 0.6327801 0.030209350
## All.Interact.X.bayesglm 0.6345052 0.015715421
## All.Interact.X.glmnet 0.6455950 0.021054446
## All.Interact.X.no.rnorm.rpart 0.6497240 0.016789805
## All.Interact.X.no.rnorm.rf 0.6698360 0.013309611
## csm.glm 0.5995009 0.023111654
## csm.bayesglm 0.6465508 0.020134659
## csm.glmnet 0.6434744 0.013857005
## csm.rpart 0.6497240 0.024681372
## csm.rf 0.6673789 0.020139770
## max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA
## Random.myrandom_classfr NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.03187391 NA
## Max.cor.Y.glm 0.01958033 868.3141
## Interact.High.cor.Y.glm 0.02147437 877.4626
## Low.cor.X.glm 0.01254642 864.8173
## All.X.glm 0.02894366 877.2778
## All.X.bayesglm 0.01087050 906.5894
## All.X.glmnet 0.03049648 NA
## All.X.no.rnorm.rpart 0.03471308 NA
## All.X.no.rnorm.rf 0.03596915 NA
## All.Interact.X.glm 0.06241819 853.2895
## All.Interact.X.bayesglm 0.03299670 883.1471
## All.Interact.X.glmnet 0.04398531 NA
## All.Interact.X.no.rnorm.rpart 0.03471308 NA
## All.Interact.X.no.rnorm.rf 0.02825810 NA
## csm.glm 0.04919951 850.4544
## csm.bayesglm 0.04297055 887.6748
## csm.glmnet 0.03112462 NA
## csm.rpart 0.05295970 NA
## csm.rf 0.04149673 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 11 fit.models_1_rf 11 0 201.596 268.199 66.604
## 12 fit.models_1_end 12 0 268.200 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 125.587 268.207 142.621
## 12 fit.models 7 2 268.208 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Max.cor.Y.glm Max.cor.Y.glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm
## Low.cor.X.glm Low.cor.X.glm
## All.X.glm All.X.glm
## All.X.bayesglm All.X.bayesglm
## All.X.glmnet All.X.glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf
## All.Interact.X.glm All.Interact.X.glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf
## csm.glm csm.glm
## csm.bayesglm csm.bayesglm
## csm.glmnet csm.glmnet
## csm.rpart csm.rpart
## csm.rf csm.rf
## model_method
## MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart rpart
## Max.cor.Y.glm glm
## Interact.High.cor.Y.glm glm
## Low.cor.X.glm glm
## All.X.glm glm
## All.X.bayesglm bayesglm
## All.X.glmnet glmnet
## All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf rf
## All.Interact.X.glm glm
## All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf rf
## csm.glm glm
## csm.bayesglm bayesglm
## csm.glmnet glmnet
## csm.rpart rpart
## csm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.diff
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.diff
## Max.cor.Y.rpart biddable, startprice.diff
## Max.cor.Y.glm biddable, startprice.diff
## Interact.High.cor.Y.glm biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*startprice.diff, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*startprice.diff, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5071756
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.9051018
## Max.cor.Y.rpart 3 0.8435581
## Max.cor.Y.glm 1 0.8706955
## Interact.High.cor.Y.glm 1 0.8716794
## Low.cor.X.glm 1 0.9063401
## All.X.glm 1 0.9130577
## All.X.bayesglm 1 0.9103774
## All.X.glmnet 9 0.8773155
## All.X.no.rnorm.rpart 3 0.8435581
## All.X.no.rnorm.rf 3 1.0000000
## All.Interact.X.glm 1 0.9285072
## All.Interact.X.bayesglm 1 0.9261281
## All.Interact.X.glmnet 9 0.9132994
## All.Interact.X.no.rnorm.rpart 3 0.8435581
## All.Interact.X.no.rnorm.rf 3 1.0000000
## csm.glm 1 0.9246141
## csm.bayesglm 1 0.9218321
## csm.glmnet 9 0.9086260
## csm.rpart 3 0.8435581
## csm.rf 3 0.9999915
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320225
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.8538283
## Max.cor.Y.rpart 0.9 0.8113440
## Max.cor.Y.glm 0.7 0.7906404
## Interact.High.cor.Y.glm 0.7 0.7915633
## Low.cor.X.glm 0.5 0.8123570
## All.X.glm 0.4 0.8217391
## All.X.bayesglm 0.6 0.8238095
## All.X.glmnet 0.6 0.8048485
## All.X.no.rnorm.rpart 0.9 0.8113440
## All.X.no.rnorm.rf 0.6 1.0000000
## All.Interact.X.glm 0.4 0.8396125
## All.Interact.X.bayesglm 0.5 0.8388571
## All.Interact.X.glmnet 0.5 0.8227115
## All.Interact.X.no.rnorm.rpart 0.9 0.8113440
## All.Interact.X.no.rnorm.rf 0.5 1.0000000
## csm.glm 0.4 0.8315098
## csm.bayesglm 0.4 0.8311404
## csm.glmnet 0.6 0.8123457
## csm.rpart 0.9 0.8113440
## csm.rf 0.5 0.9988901
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5379877 0.0000000 0.5000000
## Random.myrandom_classfr 0.4620123 0.0000000 0.5191913
## Max.cor.Y.cv.0.rpart 0.5379877 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8706366 0.7382100 0.8812811
## Max.cor.Y.rpart 0.8367648 0.6685851 0.8273068
## Max.cor.Y.glm 0.7977525 0.5911469 0.8658999
## Interact.High.cor.Y.glm 0.7936467 0.5830988 0.8652888
## Low.cor.X.glm 0.7854194 0.5673801 0.8499512
## All.X.glm 0.7905476 0.5768469 0.8492837
## All.X.bayesglm 0.7946660 0.5859868 0.8576175
## All.X.glmnet 0.7977588 0.5910839 0.8631682
## All.X.no.rnorm.rpart 0.8347135 0.6642538 0.8273068
## All.X.no.rnorm.rf 0.8418803 0.6791071 0.8876893
## All.Interact.X.glm 0.7844033 0.5662735 0.8559230
## All.Interact.X.bayesglm 0.8018519 0.6010074 0.8673582
## All.Interact.X.glmnet 0.8038936 0.6039602 0.8728883
## All.Interact.X.no.rnorm.rpart 0.8347135 0.6642538 0.8273068
## All.Interact.X.no.rnorm.rf 0.8460019 0.6873959 0.8910295
## csm.glm 0.7884995 0.5731278 0.8425315
## csm.bayesglm 0.8059576 0.6084395 0.8751682
## csm.glmnet 0.8080089 0.6119731 0.8701669
## csm.rpart 0.8419120 0.6771481 0.8273068
## csm.rf 0.8305983 0.6567546 0.8875302
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6332046
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.7994859
## Max.cor.Y.rpart 0.9 0.7934783
## Max.cor.Y.glm 0.7 0.7896175
## Interact.High.cor.Y.glm 0.7 0.7868852
## Low.cor.X.glm 0.7 0.7765668
## All.X.glm 0.6 0.7769029
## All.X.bayesglm 0.6 0.7830688
## All.X.glmnet 0.6 0.7962213
## All.X.no.rnorm.rpart 0.9 0.7934783
## All.X.no.rnorm.rf 0.6 0.8086839
## All.Interact.X.glm 0.6 0.7894737
## All.Interact.X.bayesglm 0.5 0.7969543
## All.Interact.X.glmnet 0.6 0.7930108
## All.Interact.X.no.rnorm.rpart 0.9 0.7934783
## All.Interact.X.no.rnorm.rf 0.5 0.8120104
## csm.glm 0.5 0.7747748
## csm.bayesglm 0.6 0.7973684
## csm.glmnet 0.6 0.7925033
## csm.rpart 0.9 0.7934783
## csm.rf 0.5 0.8184080
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5367232 0.0000000
## Random.myrandom_classfr 0.4632768 0.0000000
## Max.cor.Y.cv.0.rpart 0.5367232 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8237288 0.6430437
## Max.cor.Y.rpart 0.8282486 0.6497240
## Max.cor.Y.glm 0.8259887 0.6448747
## Interact.High.cor.Y.glm 0.8237288 0.6402627
## Low.cor.X.glm 0.8146893 0.6219426
## All.X.glm 0.8079096 0.6099603
## All.X.bayesglm 0.8146893 0.6233456
## All.X.glmnet 0.8293785 0.6523227
## All.X.no.rnorm.rpart 0.8282486 0.6497240
## All.X.no.rnorm.rf 0.8406780 0.6751279
## All.Interact.X.glm 0.8192090 0.6327801
## All.Interact.X.bayesglm 0.8192090 0.6345052
## All.Interact.X.glmnet 0.8259887 0.6455950
## All.Interact.X.no.rnorm.rpart 0.8282486 0.6497240
## All.Interact.X.no.rnorm.rf 0.8372881 0.6698360
## csm.glm 0.8022599 0.5995009
## csm.bayesglm 0.8259887 0.6465508
## csm.glmnet 0.8248588 0.6434744
## csm.rpart 0.8282486 0.6497240
## csm.rf 0.8350282 0.6673789
## inv.elapsedtime.everything
## MFO.myMFO_classfr 2.39808153
## Random.myrandom_classfr 3.54609929
## Max.cor.Y.cv.0.rpart 1.57728707
## Max.cor.Y.cv.0.cp.0.rpart 2.08333333
## Max.cor.Y.rpart 0.94607379
## Max.cor.Y.glm 1.02986612
## Interact.High.cor.Y.glm 0.95238095
## Low.cor.X.glm 0.72150072
## All.X.glm 0.63653724
## All.X.bayesglm 0.38654813
## All.X.glmnet 0.19168104
## All.X.no.rnorm.rpart 0.59772863
## All.X.no.rnorm.rf 0.06491820
## All.Interact.X.glm 0.54466231
## All.Interact.X.bayesglm 0.45913682
## All.Interact.X.glmnet 0.15974441
## All.Interact.X.no.rnorm.rpart 0.57504313
## All.Interact.X.no.rnorm.rf 0.05474353
## csm.glm 0.52410901
## csm.bayesglm 0.41701418
## csm.glmnet 0.33211558
## csm.rpart 0.55803571
## csm.rf 0.05935423
## inv.elapsedtime.final inv.aic.fit
## MFO.myMFO_classfr 333.3333333 NA
## Random.myrandom_classfr 500.0000000 NA
## Max.cor.Y.cv.0.rpart 83.3333333 NA
## Max.cor.Y.cv.0.cp.0.rpart 111.1111111 NA
## Max.cor.Y.rpart 90.9090909 NA
## Max.cor.Y.glm 76.9230769 0.001151657
## Interact.High.cor.Y.glm 66.6666667 0.001139650
## Low.cor.X.glm 5.9523810 0.001156314
## All.X.glm 4.0000000 0.001139890
## All.X.bayesglm 2.7397260 0.001103035
## All.X.glmnet 1.0405827 NA
## All.X.no.rnorm.rpart 15.8730159 NA
## All.X.no.rnorm.rf 0.2082899 NA
## All.Interact.X.glm 2.6809651 0.001171935
## All.Interact.X.bayesglm 1.9801980 0.001132314
## All.Interact.X.glmnet 0.6849315 NA
## All.Interact.X.no.rnorm.rpart 12.9870130 NA
## All.Interact.X.no.rnorm.rf 0.1842978 NA
## csm.glm 3.2679739 0.001175842
## csm.bayesglm 2.2172949 0.001126539
## csm.glmnet 2.8089888 NA
## csm.rpart 10.6382979 NA
## csm.rf 0.1757778 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 247 rows containing missing values (geom_point).## Warning: Removed 14 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB
## 13 All.X.no.rnorm.rf 0.8406780 0.8876893
## 18 All.Interact.X.no.rnorm.rf 0.8372881 0.8910295
## 23 csm.rf 0.8350282 0.8875302
## 11 All.X.glmnet 0.8293785 0.8631682
## 5 Max.cor.Y.rpart 0.8282486 0.8273068
## 12 All.X.no.rnorm.rpart 0.8282486 0.8273068
## 17 All.Interact.X.no.rnorm.rpart 0.8282486 0.8273068
## 22 csm.rpart 0.8282486 0.8273068
## 20 csm.bayesglm 0.8259887 0.8751682
## 16 All.Interact.X.glmnet 0.8259887 0.8728883
## 6 Max.cor.Y.glm 0.8259887 0.8658999
## 21 csm.glmnet 0.8248588 0.8701669
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.8237288 0.8812811
## 7 Interact.High.cor.Y.glm 0.8237288 0.8652888
## 15 All.Interact.X.bayesglm 0.8192090 0.8673582
## 14 All.Interact.X.glm 0.8192090 0.8559230
## 10 All.X.bayesglm 0.8146893 0.8576175
## 8 Low.cor.X.glm 0.8146893 0.8499512
## 9 All.X.glm 0.8079096 0.8492837
## 19 csm.glm 0.8022599 0.8425315
## 1 MFO.myMFO_classfr 0.5367232 0.5000000
## 3 Max.cor.Y.cv.0.rpart 0.5367232 0.5000000
## 2 Random.myrandom_classfr 0.4632768 0.5191913
## max.Kappa.OOB min.aic.fit opt.prob.threshold.OOB
## 13 0.6751279 NA 0.6
## 18 0.6698360 NA 0.5
## 23 0.6673789 NA 0.5
## 11 0.6523227 NA 0.6
## 5 0.6497240 NA 0.9
## 12 0.6497240 NA 0.9
## 17 0.6497240 NA 0.9
## 22 0.6497240 NA 0.9
## 20 0.6465508 887.6748 0.6
## 16 0.6455950 NA 0.6
## 6 0.6448747 868.3141 0.7
## 21 0.6434744 NA 0.6
## 4 0.6430437 NA 0.6
## 7 0.6402627 877.4626 0.7
## 15 0.6345052 883.1471 0.5
## 14 0.6327801 853.2895 0.6
## 10 0.6233456 906.5894 0.6
## 8 0.6219426 864.8173 0.7
## 9 0.6099603 877.2778 0.6
## 19 0.5995009 850.4544 0.5
## 1 0.0000000 NA 0.5
## 3 0.0000000 NA 0.5
## 2 0.0000000 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.## Warning: Removed 105 rows containing missing values (geom_point).## Warning: Removed 14 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.no.rnorm.rf"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 90 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 90 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
predct_error_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".err")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## startprice.diff 1.000000e+02
## biddable 9.829657e+01
## idseq.my 3.855844e+01
## D.ratio.sum.TfIdf.nwrds 4.710374e+00
## D.TfIdf.sum.stem.stop.Ratio 4.562101e+00
## storage.fctr64 3.804173e+00
## prdline.my.fctriPadAir 3.449252e+00
## D.ratio.nstopwrds.nwrds 3.419359e+00
## D.TfIdf.sum.post.stop 3.400298e+00
## D.TfIdf.sum.post.stem 3.284824e+00
## D.sum.TfIdf 3.112166e+00
## color.fctrWhite 3.046840e+00
## condition.fctrNew 3.035508e+00
## D.nstopwrds.log 2.833335e+00
## color.fctrBlack 2.763335e+00
## D.nchrs.log 2.755461e+00
## prdline.my.fctriPadmini 2.707907e+00
## storage.fctr16 2.673851e+00
## prdline.my.fctriPad 3+ 2.507217e+00
## storage.fctrUnknown 2.175406e+00
## carrier.fctrUnknown 2.165420e+00
## cellular.fctr1 2.079886e+00
## carrier.fctrVerizon 2.006626e+00
## condition.fctrNew other (see details) 2.004513e+00
## D.nwrds.log 2.002785e+00
## D.nuppr.log 1.984142e+00
## prdline.my.fctriPad 1 1.948124e+00
## prdline.my.fctriPadmini 2+ 1.870453e+00
## cellular.fctrUnknown 1.772779e+00
## carrier.fctrAT&T 1.762314e+00
## color.fctrSpace Gray 1.726675e+00
## storage.fctr32 1.723320e+00
## color.fctrGold 1.701581e+00
## condition.fctrFor parts or not working 1.644323e+00
## D.nwrds.unq.log 1.566584e+00
## prdline.my.fctriPad 2 1.437702e+00
## D.terms.n.post.stem.log 1.433424e+00
## D.terms.n.post.stop.log 1.357900e+00
## D.terms.n.post.stem 1.341172e+00
## D.terms.n.post.stop 1.274846e+00
## carrier.fctrSprint 1.239758e+00
## condition.fctrManufacturer refurbished 1.122589e+00
## D.npnct05.log 9.921267e-01
## D.npnct13.log 9.182885e-01
## D.terms.n.stem.stop.Ratio 8.198376e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 8.044334e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 7.897332e-01
## D.npnct11.log 7.287454e-01
## D.ndgts.log 7.174305e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 6.328956e-01
## D.npnct08.log 4.858434e-01
## D.npnct15.log 4.543304e-01
## D.npnct12.log 4.286325e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.213698e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 4.109719e-01
## D.npnct01.log 3.942874e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.942077e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.808551e-01
## condition.fctrSeller refurbished 3.534679e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 3.318202e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 3.059793e-01
## D.npnct24.log 2.769820e-01
## D.npnct14.log 2.220022e-01
## carrier.fctrT-Mobile 1.809466e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694272e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.678059e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 1.560424e-01
## D.npnct16.log 1.348258e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.323776e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.291254e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.142109e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.796609e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 9.518040e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 8.424913e-02
## D.npnct06.log 6.927794e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 5.249594e-02
## D.npnct03.log 4.964707e-02
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.901452e-02
## D.npnct10.log 2.429697e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 1.694124e-02
## carrier.fctrOther 1.405819e-02
## D.npnct28.log 4.760632e-03
## D.npnct09.log 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## All.X.no.rnorm.rf.importance
## startprice.diff 1.000000e+02
## biddable 9.829657e+01
## idseq.my 3.855844e+01
## D.ratio.sum.TfIdf.nwrds 4.710374e+00
## D.TfIdf.sum.stem.stop.Ratio 4.562101e+00
## storage.fctr64 3.804173e+00
## prdline.my.fctriPadAir 3.449252e+00
## D.ratio.nstopwrds.nwrds 3.419359e+00
## D.TfIdf.sum.post.stop 3.400298e+00
## D.TfIdf.sum.post.stem 3.284824e+00
## D.sum.TfIdf 3.112166e+00
## color.fctrWhite 3.046840e+00
## condition.fctrNew 3.035508e+00
## D.nstopwrds.log 2.833335e+00
## color.fctrBlack 2.763335e+00
## D.nchrs.log 2.755461e+00
## prdline.my.fctriPadmini 2.707907e+00
## storage.fctr16 2.673851e+00
## prdline.my.fctriPad 3+ 2.507217e+00
## storage.fctrUnknown 2.175406e+00
## carrier.fctrUnknown 2.165420e+00
## cellular.fctr1 2.079886e+00
## carrier.fctrVerizon 2.006626e+00
## condition.fctrNew other (see details) 2.004513e+00
## D.nwrds.log 2.002785e+00
## D.nuppr.log 1.984142e+00
## prdline.my.fctriPad 1 1.948124e+00
## prdline.my.fctriPadmini 2+ 1.870453e+00
## cellular.fctrUnknown 1.772779e+00
## carrier.fctrAT&T 1.762314e+00
## color.fctrSpace Gray 1.726675e+00
## storage.fctr32 1.723320e+00
## color.fctrGold 1.701581e+00
## condition.fctrFor parts or not working 1.644323e+00
## D.nwrds.unq.log 1.566584e+00
## prdline.my.fctriPad 2 1.437702e+00
## D.terms.n.post.stem.log 1.433424e+00
## D.terms.n.post.stop.log 1.357900e+00
## D.terms.n.post.stem 1.341172e+00
## D.terms.n.post.stop 1.274846e+00
## carrier.fctrSprint 1.239758e+00
## condition.fctrManufacturer refurbished 1.122589e+00
## D.npnct05.log 9.921267e-01
## D.npnct13.log 9.182885e-01
## D.terms.n.stem.stop.Ratio 8.198376e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 8.044334e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 7.897332e-01
## D.npnct11.log 7.287454e-01
## D.ndgts.log 7.174305e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 6.328956e-01
## D.npnct08.log 4.858434e-01
## D.npnct15.log 4.543304e-01
## D.npnct12.log 4.286325e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.213698e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 4.109719e-01
## D.npnct01.log 3.942874e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.942077e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.808551e-01
## condition.fctrSeller refurbished 3.534679e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 3.318202e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 3.059793e-01
## D.npnct24.log 2.769820e-01
## D.npnct14.log 2.220022e-01
## carrier.fctrT-Mobile 1.809466e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694272e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.678059e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 1.560424e-01
## D.npnct16.log 1.348258e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.323776e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.291254e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.142109e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.796609e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 9.518040e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 8.424913e-02
## D.npnct06.log 6.927794e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 5.249594e-02
## D.npnct03.log 4.964707e-02
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.901452e-02
## D.npnct10.log 2.429697e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 1.694124e-02
## carrier.fctrOther 1.405819e-02
## D.npnct28.log 4.760632e-03
## D.npnct09.log 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 42
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 66 10066 N 0.490
## 594 10594 N 0.474
## 1396 11397 N 0.002
## 512 10512 N 0.634
## 508 10508 N 0.642
## 819 10819 N 0.662
## 1103 11103 N 0.664
## 1836 11837 N 0.764
## 1835 11836 N 0.802
## 747 10747 N 0.850
## 526 10526 N 0.868
## 1768 11769 N 0.888
## 1699 11700 N 0.946
## 199 10199 N 0.996
## 283 10283 N 0.998
## sold.fctr.predict.All.X.no.rnorm.rf
## 66 N
## 594 N
## 1396 N
## 512 Y
## 508 Y
## 819 Y
## 1103 Y
## 1836 Y
## 1835 Y
## 747 Y
## 526 Y
## 1768 Y
## 1699 Y
## 199 Y
## 283 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 66 TRUE
## 594 TRUE
## 1396 TRUE
## 512 FALSE
## 508 FALSE
## 819 FALSE
## 1103 FALSE
## 1836 FALSE
## 1835 FALSE
## 747 FALSE
## 526 FALSE
## 1768 FALSE
## 1699 FALSE
## 199 FALSE
## 283 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error .label
## 66 0.000 10066
## 594 0.000 10594
## 1396 0.000 11397
## 512 0.034 10512
## 508 0.042 10508
## 819 0.062 10819
## 1103 0.064 11103
## 1836 0.164 11837
## 1835 0.202 11836
## 747 0.250 10747
## 526 0.268 10526
## 1768 0.288 11769
## 1699 0.346 11700
## 199 0.396 10199
## 283 0.398 10283
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 1358 11359 Y 0.000
## 1447 11448 Y 0.000
## 1696 11697 Y 0.008
## 1582 11583 Y 0.014
## 1212 11212 Y 0.022
## 1420 11421 Y 0.026
## sold.fctr.predict.All.X.no.rnorm.rf
## 1358 N
## 1447 N
## 1696 N
## 1582 N
## 1212 N
## 1420 N
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 1358 FALSE
## 1447 FALSE
## 1696 FALSE
## 1582 FALSE
## 1212 FALSE
## 1420 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 1358 -0.600
## 1447 -0.600
## 1696 -0.592
## 1582 -0.586
## 1212 -0.578
## 1420 -0.574
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 1704 11705 Y 0.114
## 1240 11241 Y 0.154
## 961 10961 Y 0.362
## 99 10099 Y 0.384
## 508 10508 N 0.642
## 120 10120 N 0.698
## sold.fctr.predict.All.X.no.rnorm.rf
## 1704 N
## 1240 N
## 961 N
## 99 N
## 508 Y
## 120 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 1704 FALSE
## 1240 FALSE
## 961 FALSE
## 99 FALSE
## 508 FALSE
## 120 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 1704 -0.486
## 1240 -0.446
## 961 -0.238
## 99 -0.216
## 508 0.042
## 120 0.098
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 491 10491 N 0.976
## 955 10955 N 0.994
## 1621 11622 N 0.994
## 199 10199 N 0.996
## 283 10283 N 0.998
## 488 10488 N 1.000
## sold.fctr.predict.All.X.no.rnorm.rf
## 491 Y
## 955 Y
## 1621 Y
## 199 Y
## 283 Y
## 488 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 491 FALSE
## 955 FALSE
## 1621 FALSE
## 199 FALSE
## 283 FALSE
## 488 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 491 0.376
## 955 0.394
## 1621 0.394
## 199 0.396
## 283 0.398
## 488 0.400
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 268.208 298.358 30.15
## 13 fit.models 7 3 298.358 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.All.X.no.rnorm.rf.prob"
## [2] "sold.fctr.predict.All.X.no.rnorm.rf"
## [3] "sold.fctr.predict.All.X.no.rnorm.rf.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 298.358 303.548 5.19
## 14 fit.data.training 8 0 303.548 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## [1] "fitting model: Final.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1859 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 3718 matrix numeric
## oob.times 1859 -none- numeric
## classes 2 -none- character
## importance 90 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 1859 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 90 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6325855
## 2 0.1 0.8456244
## 3 0.2 0.9513274
## 4 0.3 0.9839817
## 5 0.4 0.9988386
## 6 0.5 1.0000000
## 7 0.6 0.9976690
## 8 0.7 0.9563107
## 9 0.8 0.8802083
## 10 0.9 0.8047255
## 11 1.0 0.1932773## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Final.rf.N sold.fctr.predict.Final.rf.Y
## 1 N 999 NA
## 2 Y NA 860
## Prediction
## Reference N Y
## N 999 0
## Y 0 860
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.0000000 1.0000000 0.9980176 1.0000000 0.5373857
## AccuracyPValue McnemarPValue
## 0.0000000 NaN## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.nstopwrds.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.terms.n.post.stop.log, D.nwrds.unq.log, D.terms.n.post.stem.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 17.792 10.135
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.8321703
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9980176 1 0.659661
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.006940991 0.01364851rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 303.548 323.644 20.096
## 15 fit.data.training 8 1 323.645 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.6sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.X.no.rnorm.rf.importance
## startprice.diff 1.000000e+02
## biddable 9.829657e+01
## idseq.my 3.855844e+01
## D.TfIdf.sum.stem.stop.Ratio 4.562101e+00
## D.ratio.sum.TfIdf.nwrds 4.710374e+00
## prdline.my.fctriPadAir 3.449252e+00
## D.ratio.nstopwrds.nwrds 3.419359e+00
## condition.fctrNew 3.035508e+00
## color.fctrWhite 3.046840e+00
## prdline.my.fctriPad 1 1.948124e+00
## D.TfIdf.sum.post.stop 3.400298e+00
## D.nchrs.log 2.755461e+00
## D.sum.TfIdf 3.112166e+00
## D.TfIdf.sum.post.stem 3.284824e+00
## prdline.my.fctriPad 3+ 2.507217e+00
## D.nstopwrds.log 2.833335e+00
## storage.fctr64 3.804173e+00
## storage.fctr16 2.673851e+00
## cellular.fctr1 2.079886e+00
## color.fctrBlack 2.763335e+00
## prdline.my.fctriPadmini 2+ 1.870453e+00
## D.nuppr.log 1.984142e+00
## prdline.my.fctriPadmini 2.707907e+00
## color.fctrSpace Gray 1.726675e+00
## D.nwrds.log 2.002785e+00
## carrier.fctrUnknown 2.165420e+00
## storage.fctrUnknown 2.175406e+00
## prdline.my.fctriPad 2 1.437702e+00
## cellular.fctrUnknown 1.772779e+00
## condition.fctrNew other (see details) 2.004513e+00
## storage.fctr32 1.723320e+00
## condition.fctrFor parts or not working 1.644323e+00
## carrier.fctrVerizon 2.006626e+00
## D.terms.n.post.stem.log 1.433424e+00
## carrier.fctrAT&T 1.762314e+00
## D.terms.n.post.stem 1.341172e+00
## D.npnct11.log 7.287454e-01
## D.terms.n.post.stop.log 1.357900e+00
## D.terms.n.post.stop 1.274846e+00
## color.fctrGold 1.701581e+00
## D.nwrds.unq.log 1.566584e+00
## D.npnct13.log 9.182885e-01
## D.ndgts.log 7.174305e-01
## D.npnct15.log 4.543304e-01
## condition.fctrManufacturer refurbished 1.122589e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 7.897332e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 8.044334e-01
## carrier.fctrSprint 1.239758e+00
## D.terms.n.stem.stop.Ratio 8.198376e-01
## condition.fctrSeller refurbished 3.534679e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 9.518040e-02
## prdline.my.fctriPad 3+:.clusterid.fctr3 6.328956e-01
## D.npnct05.log 9.921267e-01
## carrier.fctrT-Mobile 1.809466e-01
## D.npnct08.log 4.858434e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 8.424913e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 3.808551e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.323776e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.942077e-01
## D.npnct01.log 3.942874e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.796609e-02
## D.npnct16.log 1.348258e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 3.318202e-01
## D.npnct14.log 2.220022e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 1.560424e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.678059e-01
## D.npnct12.log 4.286325e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.142109e-01
## D.npnct24.log 2.769820e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 3.059793e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.901452e-02
## D.npnct06.log 6.927794e-02
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694272e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.291254e-01
## D.npnct10.log 2.429697e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 1.694124e-02
## prdline.my.fctriPadAir:.clusterid.fctr3 4.109719e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.213698e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 5.249594e-02
## D.npnct03.log 4.964707e-02
## carrier.fctrOther 1.405819e-02
## D.npnct09.log 0.000000e+00
## D.npnct28.log 4.760632e-03
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## importance
## startprice.diff 1.000000e+02
## biddable 9.714977e+01
## idseq.my 3.655118e+01
## D.TfIdf.sum.stem.stop.Ratio 4.111189e+00
## D.ratio.sum.TfIdf.nwrds 4.029252e+00
## prdline.my.fctriPadAir 3.604291e+00
## D.ratio.nstopwrds.nwrds 3.129337e+00
## condition.fctrNew 3.114490e+00
## color.fctrWhite 3.083885e+00
## prdline.my.fctriPad 1 3.047200e+00
## D.TfIdf.sum.post.stop 2.909248e+00
## D.nchrs.log 2.779848e+00
## D.sum.TfIdf 2.690770e+00
## D.TfIdf.sum.post.stem 2.622665e+00
## prdline.my.fctriPad 3+ 2.600492e+00
## D.nstopwrds.log 2.589084e+00
## storage.fctr64 2.509445e+00
## storage.fctr16 2.500840e+00
## cellular.fctr1 2.436548e+00
## color.fctrBlack 2.396730e+00
## prdline.my.fctriPadmini 2+ 2.371923e+00
## D.nuppr.log 2.315793e+00
## prdline.my.fctriPadmini 2.212984e+00
## color.fctrSpace Gray 2.133812e+00
## D.nwrds.log 2.028829e+00
## carrier.fctrUnknown 1.958878e+00
## storage.fctrUnknown 1.942625e+00
## prdline.my.fctriPad 2 1.908280e+00
## cellular.fctrUnknown 1.895589e+00
## condition.fctrNew other (see details) 1.632602e+00
## storage.fctr32 1.581651e+00
## condition.fctrFor parts or not working 1.493784e+00
## carrier.fctrVerizon 1.492578e+00
## D.terms.n.post.stem.log 1.422731e+00
## carrier.fctrAT&T 1.363287e+00
## D.terms.n.post.stem 1.360814e+00
## D.npnct11.log 1.304090e+00
## D.terms.n.post.stop.log 1.301554e+00
## D.terms.n.post.stop 1.294931e+00
## color.fctrGold 1.238760e+00
## D.nwrds.unq.log 1.236413e+00
## D.npnct13.log 1.079486e+00
## D.ndgts.log 1.076077e+00
## D.npnct15.log 9.980478e-01
## condition.fctrManufacturer refurbished 7.688005e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 6.998488e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 6.972380e-01
## carrier.fctrSprint 6.740771e-01
## D.terms.n.stem.stop.Ratio 6.401453e-01
## condition.fctrSeller refurbished 5.779163e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 5.581525e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 5.197071e-01
## D.npnct05.log 5.168400e-01
## carrier.fctrT-Mobile 5.012177e-01
## D.npnct08.log 4.044977e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 3.872699e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.730643e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 3.558532e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.380535e-01
## D.npnct01.log 3.231315e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.074205e-01
## D.npnct16.log 2.949843e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.902052e-01
## D.npnct14.log 2.823363e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 2.177715e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.104155e-01
## D.npnct12.log 1.985962e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.827763e-01
## D.npnct24.log 1.773042e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 1.749152e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 1.727758e-01
## D.npnct06.log 1.668731e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.666519e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.637150e-01
## D.npnct10.log 1.633006e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 1.501172e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.453009e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 1.244787e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 7.102360e-02
## D.npnct03.log 6.886715e-02
## carrier.fctrOther 6.336752e-02
## D.npnct09.log 9.683639e-04
## D.npnct28.log 7.262729e-04
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## Final.rf.importance
## startprice.diff 1.000000e+02
## biddable 9.714977e+01
## idseq.my 3.655118e+01
## D.TfIdf.sum.stem.stop.Ratio 4.111189e+00
## D.ratio.sum.TfIdf.nwrds 4.029252e+00
## prdline.my.fctriPadAir 3.604291e+00
## D.ratio.nstopwrds.nwrds 3.129337e+00
## condition.fctrNew 3.114490e+00
## color.fctrWhite 3.083885e+00
## prdline.my.fctriPad 1 3.047200e+00
## D.TfIdf.sum.post.stop 2.909248e+00
## D.nchrs.log 2.779848e+00
## D.sum.TfIdf 2.690770e+00
## D.TfIdf.sum.post.stem 2.622665e+00
## prdline.my.fctriPad 3+ 2.600492e+00
## D.nstopwrds.log 2.589084e+00
## storage.fctr64 2.509445e+00
## storage.fctr16 2.500840e+00
## cellular.fctr1 2.436548e+00
## color.fctrBlack 2.396730e+00
## prdline.my.fctriPadmini 2+ 2.371923e+00
## D.nuppr.log 2.315793e+00
## prdline.my.fctriPadmini 2.212984e+00
## color.fctrSpace Gray 2.133812e+00
## D.nwrds.log 2.028829e+00
## carrier.fctrUnknown 1.958878e+00
## storage.fctrUnknown 1.942625e+00
## prdline.my.fctriPad 2 1.908280e+00
## cellular.fctrUnknown 1.895589e+00
## condition.fctrNew other (see details) 1.632602e+00
## storage.fctr32 1.581651e+00
## condition.fctrFor parts or not working 1.493784e+00
## carrier.fctrVerizon 1.492578e+00
## D.terms.n.post.stem.log 1.422731e+00
## carrier.fctrAT&T 1.363287e+00
## D.terms.n.post.stem 1.360814e+00
## D.npnct11.log 1.304090e+00
## D.terms.n.post.stop.log 1.301554e+00
## D.terms.n.post.stop 1.294931e+00
## color.fctrGold 1.238760e+00
## D.nwrds.unq.log 1.236413e+00
## D.npnct13.log 1.079486e+00
## D.ndgts.log 1.076077e+00
## D.npnct15.log 9.980478e-01
## condition.fctrManufacturer refurbished 7.688005e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 6.998488e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 6.972380e-01
## carrier.fctrSprint 6.740771e-01
## D.terms.n.stem.stop.Ratio 6.401453e-01
## condition.fctrSeller refurbished 5.779163e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 5.581525e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 5.197071e-01
## D.npnct05.log 5.168400e-01
## carrier.fctrT-Mobile 5.012177e-01
## D.npnct08.log 4.044977e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 3.872699e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.730643e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 3.558532e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.380535e-01
## D.npnct01.log 3.231315e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.074205e-01
## D.npnct16.log 2.949843e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.902052e-01
## D.npnct14.log 2.823363e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 2.177715e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.104155e-01
## D.npnct12.log 1.985962e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.827763e-01
## D.npnct24.log 1.773042e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 1.749152e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 1.727758e-01
## D.npnct06.log 1.668731e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.666519e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.637150e-01
## D.npnct10.log 1.633006e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 1.501172e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.453009e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 1.244787e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 7.102360e-02
## D.npnct03.log 6.886715e-02
## carrier.fctrOther 6.336752e-02
## D.npnct09.log 9.683639e-04
## D.npnct28.log 7.262729e-04
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 42
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1350 11351 Y 0.550
## 1447 11448 Y 0.586
## 332 10332 Y 0.598
## 837 10837 Y 0.598
## 1 10001 N 0.170
## 2 10002 Y 0.996
## 594 10594 N 0.112
## 1396 11397 N 0.000
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1350 N FALSE
## 1447 N FALSE
## 332 N FALSE
## 837 N FALSE
## 1 N TRUE
## 2 Y TRUE
## 594 N TRUE
## 1396 N TRUE
## sold.fctr.predict.Final.rf.error .label
## 1350 -0.050 11351
## 1447 -0.014 11448
## 332 -0.002 10332
## 837 -0.002 10837
## 1 0.000 10001
## 2 0.000 10002
## 594 0.000 10594
## 1396 0.000 11397
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1350 11351 Y 0.550
## 1447 11448 Y 0.586
## 332 10332 Y 0.598
## 837 10837 Y 0.598
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1350 N FALSE
## 1447 N FALSE
## 332 N FALSE
## 837 N FALSE
## sold.fctr.predict.Final.rf.error
## 1350 -0.050
## 1447 -0.014
## 332 -0.002
## 837 -0.002
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.Final.rf.prob" "sold.fctr.predict.Final.rf"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 323.645 329.209 5.564
## 16 predict.data.new 9 0 329.209 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
# startprice.pred stuff
# tmp_allobs_df <- glb_get_predictions(glb_allobs_df, mdl_id=glb_fin_mdl_id,
# rsp_var_out=glb_rsp_var_out,
# prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
# glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
# "opt.prob.threshold.OOB"], NULL))
# rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
# tmp_allobs_df <- tmp_allobs_df[, c(glb_id_var, glb_rsp_var, rsp_var_out)]
# names(tmp_allobs_df)[3] <- glb_rsp_var_out
# write.csv(tmp_allobs_df, paste0(glb_out_pfx, "predict.csv"), row.names=FALSE)
##
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.6if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 42## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1860 11862 <NA> 0.306
## 1863 11865 <NA> 0.682
## 2094 12096 <NA> 0.414
## 2623 12625 <NA> 0.412
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1860 N NA
## 1863 Y NA
## 2094 N NA
## 2623 N NA
## sold.fctr.predict.Final.rf.error .label
## 1860 0 11862
## 1863 0 11865
## 2094 0 12096
## 2623 0 12625
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA <NA> NA
## NA.1 <NA> NA
## NA.2 <NA> NA
## NA.3 <NA> NA
## NA.4 <NA> NA
## NA.5 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.43 NA <NA> NA
## NA.98 NA <NA> NA
## NA.204 NA <NA> NA
## NA.265 NA <NA> NA
## NA.378 NA <NA> NA
## NA.756 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.43 <NA> NA
## NA.98 <NA> NA
## NA.204 <NA> NA
## NA.265 <NA> NA
## NA.378 <NA> NA
## NA.756 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.43 NA
## NA.98 NA
## NA.204 NA
## NA.265 NA
## NA.378 NA
## NA.756 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.792 NA <NA> NA
## NA.793 NA <NA> NA
## NA.794 NA <NA> NA
## NA.795 NA <NA> NA
## NA.796 NA <NA> NA
## NA.797 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.792 <NA> NA
## NA.793 <NA> NA
## NA.794 <NA> NA
## NA.795 <NA> NA
## NA.796 <NA> NA
## NA.797 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA## Warning: Removed 798 rows containing missing values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11943,
12115, 12253, 12285, 12367, 12388, 12585)
for (obs_id in glb_force_prediction_lst[["0"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11990, 11994, 11999,
12000, 12002, 12021, 12065, 12072,
12111, 12114, 12126, 12152, 12172,
12213, 12214, 12233, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 >= 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 <= 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
if (nrow(tmp_newobs_df) > 0)
stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y"), "startprice"]) >
max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
(glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
stop("startprice for some +ve predictions > 675")
}## [1] "Prediction errors in duplicates:"
## [1] UniqueID .grpid
## [3] sold.fctr.predict.Final.rf sold.0
## [5] sold.1 sold.NA
## [7] .freq Probability1
## [9] .err
## <0 rows> (or 0-length row.names)submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post_stem_words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words TfIDf terms for descr.my:"
## TfIdf term freq pos
## condit 208.1066 condit 496 122
## use 146.5910 use 291 559
## scratch 128.3886 scratch 286 457
## new 125.5866 new 156 346
## good 121.0564 good 197 233
## ipad 107.4871 ipad 232 275
## TfIdf term freq pos
## box 75.940848 box 110 82
## port 6.121431 port 8 400
## regular 4.394223 regular 5 436
## usag 3.745654 usag 3 557
## besid 2.275117 besid 1 67
## 128 1.263954 128 1 2
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - Space Gray, great condition comes with the
## [1] 123
## [1] " Top_n post_stem_words TfIDf terms for descr.my:"
## [1] TfIdf term freq pos top_n
## <0 rows> (or 0-length row.names)if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.6print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.no.rnorm.rf"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.rf"print(dim(glb_fitobs_df))## [1] 974 73print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB
## 13 All.X.no.rnorm.rf 0.8406780 0.8876893
## 18 All.Interact.X.no.rnorm.rf 0.8372881 0.8910295
## 23 csm.rf 0.8350282 0.8875302
## 11 All.X.glmnet 0.8293785 0.8631682
## 5 Max.cor.Y.rpart 0.8282486 0.8273068
## 12 All.X.no.rnorm.rpart 0.8282486 0.8273068
## 17 All.Interact.X.no.rnorm.rpart 0.8282486 0.8273068
## 22 csm.rpart 0.8282486 0.8273068
## 20 csm.bayesglm 0.8259887 0.8751682
## 16 All.Interact.X.glmnet 0.8259887 0.8728883
## 6 Max.cor.Y.glm 0.8259887 0.8658999
## 21 csm.glmnet 0.8248588 0.8701669
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.8237288 0.8812811
## 7 Interact.High.cor.Y.glm 0.8237288 0.8652888
## 15 All.Interact.X.bayesglm 0.8192090 0.8673582
## 14 All.Interact.X.glm 0.8192090 0.8559230
## 10 All.X.bayesglm 0.8146893 0.8576175
## 8 Low.cor.X.glm 0.8146893 0.8499512
## 9 All.X.glm 0.8079096 0.8492837
## 19 csm.glm 0.8022599 0.8425315
## 1 MFO.myMFO_classfr 0.5367232 0.5000000
## 3 Max.cor.Y.cv.0.rpart 0.5367232 0.5000000
## 2 Random.myrandom_classfr 0.4632768 0.5191913
## max.Kappa.OOB min.aic.fit opt.prob.threshold.OOB
## 13 0.6751279 NA 0.6
## 18 0.6698360 NA 0.5
## 23 0.6673789 NA 0.5
## 11 0.6523227 NA 0.6
## 5 0.6497240 NA 0.9
## 12 0.6497240 NA 0.9
## 17 0.6497240 NA 0.9
## 22 0.6497240 NA 0.9
## 20 0.6465508 887.6748 0.6
## 16 0.6455950 NA 0.6
## 6 0.6448747 868.3141 0.7
## 21 0.6434744 NA 0.6
## 4 0.6430437 NA 0.6
## 7 0.6402627 877.4626 0.7
## 15 0.6345052 883.1471 0.5
## 14 0.6327801 853.2895 0.6
## 10 0.6233456 906.5894 0.6
## 8 0.6219426 864.8173 0.7
## 9 0.6099603 877.2778 0.6
## 19 0.5995009 850.4544 0.5
## 1 0.0000000 NA 0.5
## 3 0.0000000 NA 0.5
## 2 0.0000000 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, glb_rsp_var,
predct_error_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "error.abs.OOB"
sOOB_ctgry_df <- dplyr::group_by(tmp_OOBobs_df, prdline.my)
sOOB_ctgry_df <- dplyr::count(sOOB_ctgry_df,
startprice.OOB.sum = sum(startprice),
err.abs.OOB.sum = sum(error.abs.OOB),
err.abs.OOB.mean = mean(error.abs.OOB))
names(sOOB_ctgry_df)[4] <- ".n.OOB"
sOOB_ctgry_df <- dplyr::ungroup(sOOB_ctgry_df)
#intersect(names(glb_ctgry_df), names(sOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, sOOB_ctgry_df, all=TRUE)
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print(glb_OOBobs_df[(glb_OOBobs_df$prdline.my == "iPadAir") &
!(glb_OOBobs_df[, predct_accurate_var_name]),
c(glb_id_var, glb_rsp_var_raw,
#"description"
"biddable", "startprice", "condition"
)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "All.X.no.rnorm.rf OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 446 29
## Y 112 298
## prdline.my .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 171 154 0.1929825 0.1932203
## 5 iPadAir 151 137 0.1716792 0.1706215
## 1 Unknown 97 87 0.1090226 0.1096045
## 4 iPad 3+ 136 123 0.1541353 0.1536723
## 2 iPad 1 99 89 0.1115288 0.1118644
## 7 iPadmini 2+ 104 94 0.1177945 0.1175141
## 6 iPadmini 127 114 0.1428571 0.1435028
## accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 3 26 145 0.8479532
## 5 24 127 0.8410596
## 1 22 75 0.7731959
## 4 20 116 0.8529412
## 2 18 81 0.8181818
## 7 18 86 0.8269231
## 6 13 114 0.8976378
## UniqueID sold biddable startprice condition
## 261 10261 0 1 250.00 Used
## 738 10738 0 1 350.00 New
## 19 10019 1 0 375.00 Used
## 109 10109 1 0 339.99 New
## 205 10205 1 0 415.00 Used
## 277 10277 1 0 300.00 Used
## 577 10577 1 1 279.00 Used
## 625 10625 1 0 559.99 Used
## 675 10675 1 0 280.00 Used
## 1042 11042 1 1 528.00 New
## 1053 11053 1 1 450.00 Used
## 1129 11129 1 1 350.00 New other (see details)
## 1200 11200 1 0 379.99 Used
## 1212 11212 1 0 450.00 New
## 1225 11225 1 0 499.99 Used
## 1259 11260 1 1 260.00 New other (see details)
## 1349 11350 1 0 499.00 Used
## 1353 11354 1 0 300.00 Used
## 1381 11382 1 0 439.99 New
## 1496 11497 1 0 320.00 Manufacturer refurbished
## 1521 11522 1 0 419.00 New
## 1604 11605 1 0 229.00 For parts or not working
## 1790 11791 1 1 349.99 New
## 1853 11855 1 0 424.99 Useddsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.X.no.rnorm.rf.importance
## startprice.diff 1.000000e+02
## biddable 9.829657e+01
## idseq.my 3.855844e+01
## D.ratio.sum.TfIdf.nwrds 4.710374e+00
## D.TfIdf.sum.stem.stop.Ratio 4.562101e+00
## storage.fctr64 3.804173e+00
## prdline.my.fctriPadAir 3.449252e+00
## D.ratio.nstopwrds.nwrds 3.419359e+00
## D.TfIdf.sum.post.stop 3.400298e+00
## D.TfIdf.sum.post.stem 3.284824e+00
## D.sum.TfIdf 3.112166e+00
## color.fctrWhite 3.046840e+00
## condition.fctrNew 3.035508e+00
## D.nstopwrds.log 2.833335e+00
## color.fctrBlack 2.763335e+00
## D.nchrs.log 2.755461e+00
## prdline.my.fctriPadmini 2.707907e+00
## storage.fctr16 2.673851e+00
## prdline.my.fctriPad 3+ 2.507217e+00
## storage.fctrUnknown 2.175406e+00
## carrier.fctrUnknown 2.165420e+00
## cellular.fctr1 2.079886e+00
## carrier.fctrVerizon 2.006626e+00
## condition.fctrNew other (see details) 2.004513e+00
## D.nwrds.log 2.002785e+00
## D.nuppr.log 1.984142e+00
## prdline.my.fctriPad 1 1.948124e+00
## prdline.my.fctriPadmini 2+ 1.870453e+00
## cellular.fctrUnknown 1.772779e+00
## carrier.fctrAT&T 1.762314e+00
## color.fctrSpace Gray 1.726675e+00
## storage.fctr32 1.723320e+00
## color.fctrGold 1.701581e+00
## condition.fctrFor parts or not working 1.644323e+00
## D.nwrds.unq.log 1.566584e+00
## prdline.my.fctriPad 2 1.437702e+00
## D.terms.n.post.stem.log 1.433424e+00
## D.terms.n.post.stop.log 1.357900e+00
## D.terms.n.post.stem 1.341172e+00
## D.terms.n.post.stop 1.274846e+00
## carrier.fctrSprint 1.239758e+00
## condition.fctrManufacturer refurbished 1.122589e+00
## D.npnct05.log 9.921267e-01
## D.npnct13.log 9.182885e-01
## D.terms.n.stem.stop.Ratio 8.198376e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 8.044334e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 7.897332e-01
## D.npnct11.log 7.287454e-01
## D.ndgts.log 7.174305e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 6.328956e-01
## D.npnct08.log 4.858434e-01
## D.npnct15.log 4.543304e-01
## D.npnct12.log 4.286325e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.213698e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 4.109719e-01
## D.npnct01.log 3.942874e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.942077e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.808551e-01
## condition.fctrSeller refurbished 3.534679e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 3.318202e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 3.059793e-01
## D.npnct24.log 2.769820e-01
## D.npnct14.log 2.220022e-01
## carrier.fctrT-Mobile 1.809466e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694272e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.678059e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 1.560424e-01
## D.npnct16.log 1.348258e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 1.323776e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.291254e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.142109e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.796609e-02
## prdline.my.fctriPad 2:.clusterid.fctr4 9.518040e-02
## prdline.my.fctrUnknown:.clusterid.fctr3 8.424913e-02
## D.npnct06.log 6.927794e-02
## prdline.my.fctriPad 2:.clusterid.fctr3 5.249594e-02
## D.npnct03.log 4.964707e-02
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.901452e-02
## D.npnct10.log 2.429697e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 1.694124e-02
## carrier.fctrOther 1.405819e-02
## D.npnct28.log 4.760632e-03
## D.npnct09.log 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## importance
## startprice.diff 1.000000e+02
## biddable 9.714977e+01
## idseq.my 3.655118e+01
## D.ratio.sum.TfIdf.nwrds 4.029252e+00
## D.TfIdf.sum.stem.stop.Ratio 4.111189e+00
## storage.fctr64 2.509445e+00
## prdline.my.fctriPadAir 3.604291e+00
## D.ratio.nstopwrds.nwrds 3.129337e+00
## D.TfIdf.sum.post.stop 2.909248e+00
## D.TfIdf.sum.post.stem 2.622665e+00
## D.sum.TfIdf 2.690770e+00
## color.fctrWhite 3.083885e+00
## condition.fctrNew 3.114490e+00
## D.nstopwrds.log 2.589084e+00
## color.fctrBlack 2.396730e+00
## D.nchrs.log 2.779848e+00
## prdline.my.fctriPadmini 2.212984e+00
## storage.fctr16 2.500840e+00
## prdline.my.fctriPad 3+ 2.600492e+00
## storage.fctrUnknown 1.942625e+00
## carrier.fctrUnknown 1.958878e+00
## cellular.fctr1 2.436548e+00
## carrier.fctrVerizon 1.492578e+00
## condition.fctrNew other (see details) 1.632602e+00
## D.nwrds.log 2.028829e+00
## D.nuppr.log 2.315793e+00
## prdline.my.fctriPad 1 3.047200e+00
## prdline.my.fctriPadmini 2+ 2.371923e+00
## cellular.fctrUnknown 1.895589e+00
## carrier.fctrAT&T 1.363287e+00
## color.fctrSpace Gray 2.133812e+00
## storage.fctr32 1.581651e+00
## color.fctrGold 1.238760e+00
## condition.fctrFor parts or not working 1.493784e+00
## D.nwrds.unq.log 1.236413e+00
## prdline.my.fctriPad 2 1.908280e+00
## D.terms.n.post.stem.log 1.422731e+00
## D.terms.n.post.stop.log 1.301554e+00
## D.terms.n.post.stem 1.360814e+00
## D.terms.n.post.stop 1.294931e+00
## carrier.fctrSprint 6.740771e-01
## condition.fctrManufacturer refurbished 7.688005e-01
## D.npnct05.log 5.168400e-01
## D.npnct13.log 1.079486e+00
## D.terms.n.stem.stop.Ratio 6.401453e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 6.972380e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 6.998488e-01
## D.npnct11.log 1.304090e+00
## D.ndgts.log 1.076077e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3 5.197071e-01
## D.npnct08.log 4.044977e-01
## D.npnct15.log 9.980478e-01
## D.npnct12.log 1.985962e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 1.244787e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.453009e-01
## D.npnct01.log 3.231315e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.380535e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.730643e-01
## condition.fctrSeller refurbished 5.779163e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.902052e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 1.749152e-01
## D.npnct24.log 1.773042e-01
## D.npnct14.log 2.823363e-01
## carrier.fctrT-Mobile 5.012177e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.666519e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.104155e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 2.177715e-01
## D.npnct16.log 2.949843e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 3.558532e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.637150e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.827763e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.074205e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 5.581525e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 3.872699e-01
## D.npnct06.log 1.668731e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 7.102360e-02
## D.npnct03.log 6.886715e-02
## prdline.my.fctriPad 3+:.clusterid.fctr2 1.727758e-01
## D.npnct10.log 1.633006e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 1.501172e-01
## carrier.fctrOther 6.336752e-02
## D.npnct28.log 7.262729e-04
## D.npnct09.log 9.683639e-04
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## Final.rf.importance
## startprice.diff 1.000000e+02
## biddable 9.714977e+01
## idseq.my 3.655118e+01
## D.ratio.sum.TfIdf.nwrds 4.029252e+00
## D.TfIdf.sum.stem.stop.Ratio 4.111189e+00
## storage.fctr64 2.509445e+00
## prdline.my.fctriPadAir 3.604291e+00
## D.ratio.nstopwrds.nwrds 3.129337e+00
## D.TfIdf.sum.post.stop 2.909248e+00
## D.TfIdf.sum.post.stem 2.622665e+00
## D.sum.TfIdf 2.690770e+00
## color.fctrWhite 3.083885e+00
## condition.fctrNew 3.114490e+00
## D.nstopwrds.log 2.589084e+00
## color.fctrBlack 2.396730e+00
## D.nchrs.log 2.779848e+00
## prdline.my.fctriPadmini 2.212984e+00
## storage.fctr16 2.500840e+00
## prdline.my.fctriPad 3+ 2.600492e+00
## storage.fctrUnknown 1.942625e+00
## carrier.fctrUnknown 1.958878e+00
## cellular.fctr1 2.436548e+00
## carrier.fctrVerizon 1.492578e+00
## condition.fctrNew other (see details) 1.632602e+00
## D.nwrds.log 2.028829e+00
## D.nuppr.log 2.315793e+00
## prdline.my.fctriPad 1 3.047200e+00
## prdline.my.fctriPadmini 2+ 2.371923e+00
## cellular.fctrUnknown 1.895589e+00
## carrier.fctrAT&T 1.363287e+00
## color.fctrSpace Gray 2.133812e+00
## storage.fctr32 1.581651e+00
## color.fctrGold 1.238760e+00
## condition.fctrFor parts or not working 1.493784e+00
## D.nwrds.unq.log 1.236413e+00
## prdline.my.fctriPad 2 1.908280e+00
## D.terms.n.post.stem.log 1.422731e+00
## D.terms.n.post.stop.log 1.301554e+00
## D.terms.n.post.stem 1.360814e+00
## D.terms.n.post.stop 1.294931e+00
## carrier.fctrSprint 6.740771e-01
## condition.fctrManufacturer refurbished 7.688005e-01
## D.npnct05.log 5.168400e-01
## D.npnct13.log 1.079486e+00
## D.terms.n.stem.stop.Ratio 6.401453e-01
## prdline.my.fctrUnknown:.clusterid.fctr2 6.972380e-01
## prdline.my.fctriPad 2:.clusterid.fctr5 6.998488e-01
## D.npnct11.log 1.304090e+00
## D.ndgts.log 1.076077e+00
## prdline.my.fctriPad 3+:.clusterid.fctr3 5.197071e-01
## D.npnct08.log 4.044977e-01
## D.npnct15.log 9.980478e-01
## D.npnct12.log 1.985962e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 1.244787e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.453009e-01
## D.npnct01.log 3.231315e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 3.380535e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.730643e-01
## condition.fctrSeller refurbished 5.779163e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.902052e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 1.749152e-01
## D.npnct24.log 1.773042e-01
## D.npnct14.log 2.823363e-01
## carrier.fctrT-Mobile 5.012177e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.666519e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.104155e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 2.177715e-01
## D.npnct16.log 2.949843e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 3.558532e-01
## prdline.my.fctriPadAir:.clusterid.fctr4 1.637150e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 1.827763e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.074205e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 5.581525e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 3.872699e-01
## D.npnct06.log 1.668731e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 7.102360e-02
## D.npnct03.log 6.886715e-02
## prdline.my.fctriPad 3+:.clusterid.fctr2 1.727758e-01
## D.npnct10.log 1.633006e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 1.501172e-01
## carrier.fctrOther 6.336752e-02
## D.npnct28.log 7.262729e-04
## D.npnct09.log 9.683639e-04
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 3+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 621 177# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 329.209 335.35 6.141
## 17 display.session.info 10 0 335.351 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 125.587 268.207 142.621
## 5 extract.features 3 0 21.414 90.455 69.042
## 12 fit.models 7 2 268.208 298.358 30.150
## 10 fit.models 7 0 99.427 125.587 26.160
## 14 fit.data.training 8 0 303.548 323.644 20.096
## 16 predict.data.new 9 0 329.209 335.350 6.141
## 15 fit.data.training 8 1 323.645 329.209 5.564
## 1 import.data 1 0 10.081 15.291 5.210
## 13 fit.models 7 3 298.358 303.548 5.190
## 2 inspect.data 2 0 15.292 20.094 4.802
## 8 select.features 5 0 94.676 98.481 3.805
## 7 manage.missing.data 4 1 91.469 94.675 3.206
## 6 cluster.data 4 0 90.456 91.469 1.013
## 9 partition.data.training 6 0 98.481 99.426 0.945
## 3 scrub.data 2 1 20.094 20.863 0.769
## 4 transform.data 2 2 20.863 21.414 0.551
## duration
## 11 142.620
## 5 69.041
## 12 30.150
## 10 26.160
## 14 20.096
## 16 6.141
## 15 5.564
## 1 5.210
## 13 5.190
## 2 4.802
## 8 3.805
## 7 3.206
## 6 1.013
## 9 0.945
## 3 0.769
## 4 0.551
## [1] "Total Elapsed Time: 335.35 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] randomForest_4.6-10 glmnet_2.0-2 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-43
## [7] rpart.plot_1.5.2 rpart_4.1-10 ROCR_1.0-7
## [10] gplots_2.17.0 sampling_2.7 tidyr_0.2.0
## [13] entropy_1.2.1 dynamicTreeCut_1.62 proxy_0.4-15
## [16] tm_0.6-2 NLP_0.1-8 stringr_1.0.0
## [19] dplyr_0.4.2 plyr_1.8.3 sqldf_0.4-10
## [22] RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [25] proto_0.3-10 reshape2_1.4.1 gdata_2.17.0
## [28] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [31] doBy_4.5-13 survival_2.38-3 caret_6.0-52
## [34] ggplot2_1.0.1 lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] splines_3.2.1 gtools_3.5.0 assertthat_0.1
## [4] stats4_3.2.1 yaml_2.1.13 slam_0.1-32
## [7] quantreg_5.11 pROC_1.8 chron_2.3-47
## [10] digest_0.6.8 RColorBrewer_1.1-2 minqa_1.2.4
## [13] colorspace_1.2-6 htmltools_0.2.6 lpSolve_5.6.11
## [16] BradleyTerry2_1.0-6 SparseM_1.6 scales_0.2.5
## [19] brglm_0.5-9 mgcv_1.8-7 car_2.0-25
## [22] nnet_7.3-10 lazyeval_0.1.10 pbkrtest_0.4-2
## [25] magrittr_1.5 evaluate_0.7 nlme_3.1-121
## [28] class_7.3-13 tools_3.2.1 formatR_1.2
## [31] munsell_0.4.2 compiler_3.2.1 e1071_1.6-6
## [34] caTools_1.17.1 nloptr_1.0.4 bitops_1.0-6
## [37] labeling_0.3 rmarkdown_0.7 gtable_0.1.2
## [40] codetools_0.2-14 abind_1.4-3 R6_2.1.0
## [43] knitr_1.10.5 KernSmooth_2.23-15 stringi_0.5-5
## [46] Rcpp_0.12.0 coda_0.17-1